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

Abstract

The embodiment of the invention provides a state detection method and device of weighing equipment, a storage medium and electronic equipment, the weighing equipment comprises a plurality of weighing sensors, and the method comprises the following steps: selecting a first sensor combination and a second sensor combination from a plurality of sensor combinations; executing the following state detection operations based on the first sensor combination and the second sensor combination: acquiring a first vehicle weight of each target vehicle in the group of target vehicles detected by the first sensor combination, and acquiring a second vehicle weight of each target vehicle detected by the second sensor combination; performing normal distribution fitting on the first weight difference corresponding to the group of target vehicles to obtain a fitted normal distribution parameter value; and performing state detection on the weighing equipment by comparing the fitted normal distribution parameter value with the target normal distribution parameter value to obtain a state detection result, so that the real-time performance of state detection of the weighing equipment can be improved.

Description

Technical Field

[0001] This application relates to the technical field of weight measurement. Specifically, it relates to a method and device for detecting the state of a weighing device, a storage medium, and an electronic device. Background Art

[0002] Currently, dynamic weighing technology can be applied to scenarios such as overloading control at entrances, detection at fixed overloading control stations, source supervision, and non-site law enforcement. The key device among them is the weighing device for weighing (for example, a dynamic truck scale). The weighing device can be realized by multiple weighing sensors plus a weighing table and / or a set of integrated weighing sensors according to a certain layout and combination. When one or more of these sensors have problems, it will affect the weighing result.

[0003] In order to ensure the accuracy of the weighing result, the operating state of the weighing device can be detected. The methods for detecting the weighing device can include periodic verification, manual periodic inspection, etc. However, the methods of periodic verification and periodic manual inspection can ensure that the weighing device is in good operating state at that time, but cannot ensure the operating state of the weighing device within the period.

[0004] It can be seen that the detection method of the weighing device in the related technology has the technical problem of poor real-time performance in detecting the device state. Summary of the Invention

[0005] Embodiments of this application provide a method and device for detecting the state of a weighing device, a storage medium, and an electronic device, so as to at least solve the technical problem of poor real-time performance in detecting the device state in the detection method of the weighing device in the related technology.

[0006] According to an embodiment of the present application, a method for detecting the state of a weighing device is provided, including: selecting a first sensor combination and a second sensor combination from a plurality of sensor combinations, where each sensor combination in the plurality of sensor combinations includes at least one weighing sensor among the plurality of weighing sensors, and different sensor combinations include at least partially different weighing sensors, and each sensor combination is used to obtain the complete vehicle weight; performing the following state detection operations based on the first sensor combination and the second sensor combination: obtaining a first vehicle weight of each target vehicle in a group of target vehicles detected by the first sensor combination, and obtaining a second vehicle weight of each target vehicle detected by the second sensor combination; performing a normal distribution fitting on a first weight difference corresponding to the group of target vehicles to obtain a fitted normal distribution parameter value, where the first weight difference corresponding to a group of target vehicles is a set of differences between the first vehicle weight of each target vehicle in the group of target vehicles and the second vehicle weight of each target vehicle; performing a state detection on the weighing device by comparing the fitted normal distribution parameter value and a target normal distribution parameter value to obtain a state detection result, where the target normal distribution parameter value is a preset normal distribution parameter value corresponding to a normal distribution satisfied by a set of differences between the vehicle weight detected by the first sensor combination and the vehicle weight detected by the second sensor combination.

[0007] According to another embodiment of the present application, a state detection device for a weighing device is provided, including: a selection unit configured to select a first sensor combination and a second sensor combination from a plurality of sensor combinations, wherein each of the plurality of sensor combinations includes at least one weighing sensor among the plurality of weighing sensors, and different sensor combinations include at least partially different weighing sensors, and each of the sensor combinations is configured to obtain a complete vehicle weight; a detection unit configured to perform the following state detection operations based on the first sensor combination and the second sensor combination: obtain a first vehicle weight of each target vehicle in a group of target vehicles detected by the first sensor combination, and obtain a second vehicle weight of each target vehicle detected by the second sensor combination; perform a normal distribution fitting on a first weight difference corresponding to the group of target vehicles to obtain a fitted normal distribution parameter value, wherein the first weight difference corresponding to the group of target vehicles is a set of differences between the first vehicle weight of each target vehicle in the group of target vehicles and the second vehicle weight of each target vehicle; perform a state detection on the weighing device by comparing the fitted normal distribution parameter value and a target normal distribution parameter value to obtain a state detection result, wherein the target normal distribution parameter value is a preset normal distribution parameter value corresponding to a normal distribution satisfied by a set of differences between the vehicle weight detected by the first sensor combination and the vehicle weight detected by the second sensor combination.

[0008] According to another embodiment of the present application, a computer-readable storage medium is further provided, in which a computer program is stored, and the computer program is configured to execute the steps in any one of the above method embodiments when running.

[0009] According to another embodiment of the present application, an electronic device is further provided, including a memory and a processor, a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0010] According to another embodiment of the present application, a computer program product is further provided, including a computer program, characterized in that the computer program implements the steps in any one of the above method embodiments when executed by a processor.

[0011] Through the embodiments provided in this application, a first sensor combination and a second sensor combination including at least partially different load cells are selected, and a first vehicle weight and a second vehicle weight of each target vehicle in a group of target vehicles detected by the first sensor combination and the second sensor combination respectively are obtained; the difference between the first vehicle weight and the second vehicle weight of each target vehicle is used as a first weight difference, and a normal distribution fitting is performed on the difference set of the first weight differences to obtain normal distribution parameter values. Thus, the normal distribution of the error difference between the vehicle weights output by the first sensor combination and the second sensor combination (i.e., the true weight of the vehicle is removed due to subtraction) can be obtained; by comparing the fitted normal distribution parameter values with preset target normal distribution parameter values, the status of the weighing device is detected to obtain a status detection result. Thus, based on the normal distribution parameter values of the error difference for status detection, real-time detection of each sensor in the weighing device within a cycle can be realized. By comparing the fitted normal distribution parameter values with the preset target normal distribution parameter values, the accuracy of the status detection result of the weighing device can be improved; furthermore, the technical problem of poor real-time performance of the detection method of the weighing device in the related art is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 FIG. is a schematic diagram of an optional status detection system for a weighing device provided by an embodiment of the present application;

[0013] Figure 2 FIG. is a schematic structural diagram of an optional electronic device provided by an embodiment of the present application;

[0014] Figure 3 FIG. is a schematic flowchart of an optional status detection method for a weighing device provided by an embodiment of the present application;

[0015] Figure 4 FIG. is a schematic structural diagram of an optional weighing device provided by an embodiment of the present application;

[0016] Figure 5 FIG. is a schematic flowchart of another optional status detection method for a weighing device according to an embodiment of the present application;

[0017] Figure 6 FIG. is a schematic flowchart of yet another optional status detection method for a weighing device provided by an embodiment of the present application;

[0018] Figure 7 FIG. is a structural block diagram of an optional status detection device for a weighing device according to an embodiment of the present application;

[0019] Figure 8 FIG. is a structural block diagram of a computer system of an optional electronic device according to an embodiment of the present application. Detailed implementation manners

[0020] To enable those skilled in the art to better understand the solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0021] It should be noted that the terms "first", "second", etc. in the description and claims of this application and the above-mentioned accompanying drawings 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 under appropriate circumstances so that the embodiments of this application described here can be implemented in an order different from those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0022] The embodiments of this application provide a method and device for detecting the state of a weighing device, a storage medium, and an electronic device, which can improve the real-time performance and accuracy of detecting the state of a weighing device. The following describes an exemplary application of the electronic device provided by the embodiments of this application. The electronic device provided by the embodiments of this application can be implemented as weighing devices such as dynamic truck scales, column load cells, narrow strip load cells, or combined load cells. The following will describe the exemplary application when the device implements a weighing device.

[0023] According to one aspect of the embodiments of this application, a method for detecting the state of a weighing device is provided. Optionally, in this embodiment, the method for detecting the state of the above-mentioned weighing device can be applied to a Figure 1 state detection system of a weighing device as shown. As Figure 1 shown, the state detection system 100 of the weighing device may include: a weighing device 101, a network 102, and a processing device 103. Among them, to support the state detection application of a weighing device, the weighing device 101 can be connected to the processing device 103 through the network 102. The network 102 may include, but is not limited to, at least one of the following: a wired network, a wireless network. The above-mentioned wired network may include, but is not limited to, at least one of the following: a wide area network, a metropolitan area network, a local area network. The above-mentioned wireless network may include, but is not limited to, at least one of the following: WIFI, Bluetooth.

[0024] In response to receiving a weighing device status detection instruction from the processing device 103, the weighing device 101 selects a first sensor combination and a second sensor combination from multiple sensor combinations. Each sensor combination in the multiple sensor combinations includes at least one weighing sensor among multiple weighing sensors, and different sensor combinations include at least partially different weighing sensors. Each sensor combination is used to obtain the complete vehicle weight. The following status detection operations are performed based on the first sensor combination and the second sensor combination: obtaining a first vehicle weight of each target vehicle in a group of target vehicles detected by the first sensor combination, and obtaining a second vehicle weight of each target vehicle detected by the second sensor combination; performing a normal distribution fitting on the first weight differences corresponding to a group of target vehicles to obtain the fitted normal distribution parameter values, where the first weight differences corresponding to a group of target vehicles are a set of differences between the first vehicle weight and the second vehicle weight of each target vehicle in the group of target vehicles; performing a status detection on the weighing device by comparing the fitted normal distribution parameter values with the target normal distribution parameter values to obtain a status detection result. The weighing device 101 returns the status detection result to the processing device 103.

[0025] The processing device 103 can also be an independent physical device (such as a physical server, a terminal, etc.), or a device cluster (such as a server cluster) or a distributed system composed of multiple physical devices, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0026] Optionally, in this embodiment, the above method for detecting the status of the weighing device may be executed by an electronic device as Figure 2 shown. As Figure 2 shown, the electronic device 200 may be the above-mentioned weighing device 101 or processing device 103. The electronic device 200 includes: at least one processor 201, at least one network interface 202, a bus system 203, and a memory 204. Each component in the electronic device 200 is coupled together through the bus system 203. It can be understood that the bus system 203 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 203 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clear illustration, in Figure 2 all kinds of buses are labeled as the bus system 203.

[0027] The processor 201 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., where the general-purpose processor can be a microprocessor or any conventional processor, etc.

[0028] The memory 204 may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid-state memory, hard disk drives, optical disk drives, etc. The memory 204 may optionally include one or more storage devices that are physically remote from the processor 201.

[0029] The memory 204 includes a volatile memory or a non-volatile memory, and may also include both volatile and non-volatile memories. The non-volatile memory may be a read-only memory (ROM), and the volatile memory may be a random access memory (RAM). The memory 204 described in the embodiment of the present application is intended to include any suitable type of memory.

[0030] In some embodiments, the memory 204 can store data to support various operations, examples of which include programs, modules, and data structures, or a subset or superset thereof, as exemplarily described below.

[0031] Operating system 2041, including system programs for processing various basic system services and performing hardware-related tasks, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks;

[0032] A network communication module 2042, used to reach other computing devices via one or more (wired or wireless) network interfaces 202, exemplary network interfaces 202 include: Bluetooth, Wireless Authentication (WiFi), and Universal Serial Bus (USB), etc.;

[0033] In some embodiments, the device provided in the embodiments of the present application can be implemented in software. Figure 2 The state detection device 2043 of the weighing device stored in the memory 204 is shown, which can be software in the form of a program and a plug-in, including the following software units: a selection unit, a detection unit, and these units are logical, so they can be arbitrarily combined or further split according to the functions implemented. The functions of each unit will be explained below.

[0034] In some embodiments, a terminal device or a server may implement the method for detecting the state of a weighing device provided in the embodiments of the present application by running a computer program. For example, the computer program may be a native program or a software module in an operating system; it may be a local (Native) application (Application, abbreviated as APP), that is, a program that needs to be installed in the operating system to run; it may also be a small program, that is, a program that only needs to be downloaded into a computer environment to run; it may also be a small program that can be embedded into any APP. In short, the above computer program may be any form of application program, module or plug-in.

[0035] Dynamic weighing technology is now widely used in overloading control at entrances, detection at fixed overloading control stations, source supervision, and non-site law enforcement. Among them, dynamic truck scales are key weighing devices. Weighing devices of the dynamic truck scale type are all realized by a set of weighing sensors plus a weighing table and / or a set of integrated weighing sensors according to a certain layout and combination. When one or more of the weighing sensors have problems, it will have an adverse impact on the weighing result.

[0036] Currently, the monitoring of weighing devices (dynamic truck scales) includes methods such as periodic verification and manual periodic inspection. However, in the related art, it is impossible to ensure real-time and accurate monitoring of weighing devices within a cycle. That is, the detection method of weighing devices in the related art has technical problems such as poor real-time performance, poor continuity, and low automation of device state detection.

[0037] Through the method for detecting the state of a weighing device provided in the embodiments of the present application, when a weighing device or a weighing sensor has a problem, it is possible to perform real-time and accurate early warning on the abnormal device or abnormal weighing sensor, so as to improve the device maintenance efficiency and ensure the continuous and stable operation of the weighing system.

[0038] Optionally, the above method for detecting the state of a weighing device may be executed independently by the server 101, or jointly executed by the weighing device 101 and the processing device 103, or executed by other processing devices other than the weighing device 101 and the processing device 103. As an optional implementation manner, taking the processing device 103 executing the method for detecting the state of a weighing device in this embodiment as an example. As Figure 3 shown, the process of the above method for detecting the state of a weighing device may include the following steps.

[0039] In step S301, a first sensor combination and a second sensor combination are selected from multiple sensor combinations. Among them, each sensor combination in the multiple sensor combinations includes at least one weighing sensor among multiple weighing sensors, and the weighing sensors included in different sensor combinations are at least partially different. Each sensor combination is used to obtain the complete vehicle weight.

[0040] When a weighing device (eg, a dynamic vehicle scale) senses the load weight, in order to detect the sensor status of the weighing sensor in real time, it is necessary to select multiple sensor combinations from multiple sensor combinations to respectively measure the vehicle weight of the current target vehicle.

[0041] Here, in order to be able to detect the status of different weighing sensors in real time, the selected first sensor combination and second sensor combination need to include at least partially different weighing sensors to obtain the target vehicle weight measured by different sensor combinations (that is, the first sensor combination and the second sensor combination contain different weighing sensor signals), and then further determine whether there is an abnormality in the weighing sensor based on the measured vehicle weight difference.

[0042] In some embodiments, for example, in a vehicle mobile weighing scenario, the weighing device may have multiple weighing sensors located in different areas (the figure shows 6 weighing sensors as an example). Figure 4 Here, the weighing device can be a dynamic truck scale composed of a group of integrated weighing sensors. The actual layout of the road surface is shown in the figure. The numbers in the figure (i.e., 1, 2, 3, 4, 5, 6) respectively represent the integrated weighing sensors in the weighing device.

[0043] Through the embodiments provided in the present application, at least some different first sensor combinations and second sensor combinations can be selected from multiple sensor combinations to obtain vehicle weights measured by different sensor combinations to facilitate subsequent sensor abnormality detection.

[0044] In step S302, the following state detection operation is performed based on the first sensor combination and the second sensor combination:

[0045] Obtaining a first vehicle weight of each target vehicle in a group of target vehicles detected by the first sensor combination, and obtaining a second vehicle weight of each target vehicle detected by the second sensor combination;

[0046] Performing normal distribution fitting on first weight differences corresponding to a group of target vehicles to obtain fitted normal distribution parameter values, wherein the first weight differences corresponding to the group of target vehicles are a set of differences between a first vehicle weight of each target vehicle in the group of target vehicles and a second vehicle weight of each target vehicle;

[0047] By comparing the fitted normal distribution parameter value with the target normal distribution parameter value, the state of the weighing equipment is detected to obtain a state detection result, wherein the target normal distribution parameter value is a preset normal distribution parameter value corresponding to the normal distribution satisfied by the difference between the vehicle weight detected by the first sensor combination and the vehicle weight detected by the second sensor combination.

[0048] In an actual application scenario, since it is difficult to accurately measure the actual vehicle weight of the target vehicle, in the embodiments of the present application, the first vehicle weight detected by the first sensor combination is subtracted from the second vehicle weight detected by the second sensor combination to subtract the actual vehicle weight of the target vehicle, obtaining a first weight difference. Here, the first weight difference is the error difference obtained by the first sensor and the second sensor when detecting the same target vehicle.

[0049] In some embodiments, by fitting the normal distribution to the first weight difference corresponding to each target vehicle, the normal distribution parameter values of the first weight difference (error difference) can be fitted. Then, a comparison is made with the target normal distribution parameter values preset for the normal distribution satisfied by the difference between the vehicle weight detected by the first sensor combination and the vehicle weight detected by the second sensor combination. That is, when the difference between the fitted normal distribution parameter values and the target normal distribution parameter values is less than the preset parameter difference threshold, a normal state detection result is obtained; when part of the difference between the fitted normal distribution parameter values and the target normal distribution parameter values is greater than or equal to the preset parameter difference threshold, an abnormal detection result is obtained, and the abnormal weighing sensor is further determined.

[0050] Through the embodiments provided by the present application, by determining the difference between the detected first vehicle weight and the second vehicle weight, the measurement error difference between the first sensor combination and the second sensor combination is obtained. According to the error difference, a normal distribution is fitted, and then compared with the preset target normal distribution parameter values, it is possible to accurately determine in real time whether there is an abnormality in the weighing device.

[0051] Through the above steps of the embodiments of the present application, a first sensor combination and a second sensor combination are selected from multiple sensor combinations. Each sensor combination in the multiple sensor combinations includes at least one load cell among multiple load cells. Different sensor combinations include at least partially different load cells. Each sensor combination is used to obtain the complete vehicle weight. Based on the first sensor combination and the second sensor combination, the following state detection operations are performed: obtaining the first vehicle weight of each target vehicle in a group of target vehicles detected by the first sensor combination, and obtaining the second vehicle weight of each target vehicle detected by the second sensor combination; performing a normal distribution fitting on the first weight differences corresponding to each group of target vehicles to obtain the fitted normal distribution parameter values, where the first weight differences corresponding to a group of target vehicles are the set of differences between the first vehicle weight of each target vehicle in the group of target vehicles and the second vehicle weight of each target vehicle; performing a state detection on the weighing device by comparing the fitted normal distribution parameter values with the target normal distribution parameter values to obtain a state detection result, where the target normal distribution parameter values are the normal distribution parameter values preset corresponding to the normal distribution satisfied by the set of differences between the vehicle weight detected by the first sensor combination and the vehicle weight detected by the second sensor combination, which can improve the real-time performance of the state detection result of the weighing device; thereby solving the technical problem that the detection method of the weighing device in the related art has poor real-time performance of device state detection.

[0052] Before selecting the first sensor combination and the second sensor combination from the multiple sensor combinations, the above method further includes:

[0053] S11, when the weighing device is in a normal state, obtaining the first vehicle weight of each reference vehicle in a group of reference vehicles detected by the first sensor combination, and obtaining the second vehicle weight of each reference vehicle detected by the second sensor combination;

[0054] S12, performing a normal distribution fitting on the weight differences corresponding to each reference vehicle to obtain the target normal distribution parameter values.

[0055] It should be noted that in the initial use stage after the weighing device is calibrated, the vehicle weight measured is the weight under normal conditions of the sensor. Therefore, the result obtained by performing a normal distribution fitting on the set of weight differences under normal conditions can be used as a reference standard (i.e., the target normal distribution parameter values).

[0056] In some embodiments, when the weighing device is in a normal state at the initial stage of use or after calibration, a first sensor combination and a second sensor combination are selected from a plurality of sensor combinations, and the vehicle weights of each reference vehicle in a group of reference vehicles are detected respectively to obtain a first reference vehicle weight and a second reference vehicle weight. The difference between the first reference vehicle weight and the second reference vehicle weight is used as the weight difference corresponding to each reference vehicle. Here, by taking the difference between the first reference vehicle weight and the second reference vehicle weight, the true vehicle weight of the reference vehicle is removed, so that the obtained weight difference is only the measurement error difference; the weight differences corresponding to each reference vehicle in a group of reference vehicles are subjected to normal distribution fitting to obtain a target normal distribution fitting (i.e., the reference standard or the reference difference threshold).

[0057] Through the embodiments provided in the present application, when the weighing device is in a normal state, it is possible to determine whether the measurement values of each sensor combination are accurate according to a group of reference vehicles. The vehicle weights of each reference vehicle in a group of reference vehicles are detected respectively by the first sensor combination and the second sensor combination, and then the corresponding target normal distribution parameter values are determined according to the weight differences corresponding to each reference vehicle in a group of reference vehicles.

[0058] Selecting the first sensor combination and the second sensor combination from a plurality of sensor combinations includes:

[0059] S21, select a pair of sensor combinations from a preset plurality of sensor combination pairs to obtain the first sensor combination and the second sensor combination.

[0060] Wherein, a plurality of sensor combination pairs include two sensor combinations in a plurality of sensor combinations. The weighing sensors included in different sensor combination pairs among the plurality of sensor combination pairs are at least partially different, and each sensor combination can obtain the complete vehicle weight.

[0061] The preset plurality of sensor combinations include two or more different sensor combinations. For example, referring to Figure 4 , Figure 4 includes 6 different sensors. Here, the preset sensor combinations can be any 2, 3, 4, or 5 sensors arranged and combined (the weighing sensors included in different sensor combinations are at least partially different).

[0062] When the weighing device is implemented as a dynamic vehicle scale, in an actual dynamic weighing scenario, referring to Figure 4 , in order to ensure the accuracy and practicability of the measurement, each sensor combination needs to include at least one left-region sensor (i.e., sensors 1 / 2 / 3) and a right-region sensor (i.e., sensors 4 / 5 / 6). The number of sensors in the two sensor combinations can be the same or different.

[0063] In some embodiments, a pair of sensor combinations is selected according to a preset selection strategy. For example, the selection strategy may be to first determine the number of sensors in each sensor combination, and then select two sensor combinations with the corresponding number; it may also be to randomly select multiple sensors and then randomly combine the multiple sensors to determine two sensor combinations; and during subsequent measurement processes, the number of times each sensor is selected is the same or similar, ensuring that each sensor is used, so as to facilitate subsequent real-time status detection of each sensor.

[0064] Through the embodiments provided in this application, a pair of sensor combinations can be selected from a preset plurality of sensor combinations for vehicle weight detection. During multiple selection processes, each sensor is selected and the number of usage times is similar, which is beneficial to subsequent real-time status detection of the sensors.

[0065] Obtaining the first vehicle weight of each target vehicle in a group of target vehicles detected by the first sensor combination, and obtaining the second vehicle weight of each target vehicle detected by the second sensor combination includes:

[0066] S31, determining the first vehicle weight of each target vehicle based on the first weight data of each target vehicle detected by each first sensor in the first sensor combination, and determining the second vehicle weight of each target vehicle based on the second weight data of each target vehicle detected by each second sensor in the second sensor combination;

[0067] Wherein, the first weight data of each target vehicle is the weight data detected by each first sensor within a target time period, the second weight data of each target vehicle is the weight data detected by each second sensor within a target time period, and the target time period is the time period between two calibration times for the periodic calibration of the weighing device.

[0068] Here, the first sensor combination and the second sensor combination each include a plurality of weighing sensors for obtaining the complete vehicle weight.

[0069] In some embodiments, referring to Figure 4 , when the weighing device is implemented as a dynamic vehicle scale, when the first sensors (or second sensors) in the first sensor combination (or second sensor combination) are respectively sensor 1, sensor 2, sensor 4, and sensor 5, and the first weight data of the target vehicle is detected and the first vehicle weight of the target vehicle is determined, it can be expressed according to formulas (1)-(2):

[0070] y = h1*(x1 + Δx1) + h2*(x2 + Δx2) + h4*(x4 + Δx4) + h5*(x5 + Δx5) (1)

[0071] y = h1 * x1 + h2 * x2 + h4 * x4 + h5 * x5 + er (2)

[0072] Wherein, y represents the first vehicle weight of the target vehicle measured by the first sensor combination, h represents the sensor gain, x represents the accurate signal corresponding to the weight output by the sensor under ideal conditions, Δx represents the interference applied to the accurate signal, and er represents the measurement error of the first sensor combination.

[0073] In some embodiments, the detected vehicle weight (including the true vehicle weight and the measurement error) is determined by multiplying the output signal of the sensor combination by the corresponding gain of the sensor combination.

[0074] For example, based on the first weight data of each target vehicle detected by each first sensor in the first sensor combination, the first vehicle weight of each target vehicle is determined, and based on the second weight data of each target vehicle detected by each second sensor in the second sensor combination, the second vehicle weight of each target vehicle is determined, which can be represented by the following formula (3):

[0075] Y(i,k) = H(i)X(i,k) + Er(i,k) (3)

[0076] Wherein, i represents the i-th type of sensor combination, k represents the k-th vehicle of the target vehicle, H(i) represents the corresponding gain combination of the i-th type of sensor combination, X(i,k) represents the output signal of the i-th type of sensor combination of the weighing device when measuring the k-th vehicle, and Er(i,k) represents the measurement error of the i-th type of sensor combination of the weighing device when measuring the k-th vehicle.

[0077] In some embodiments, in electronics, the gain is generally the ratio of the signal output to the signal input of a system; the gain corresponding to the sensor combination is the magnification of the original signal output of the sensor combination.

[0078] It should be noted that, in the ideal case, when the vehicle presses on each sensor, a corresponding physical signal is output by the corresponding sensor. The detected sensor has an accurate gain, so that the signal of each sensor multiplied by the gain is the detected weight when the vehicle presses. If it is a sensor combination composed of multiple sensors, then multiply each sensor by the gain respectively and then perform weighted summation.

[0079] Through the embodiments provided in this application, the vehicle weight measurement method of each target vehicle is described according to the above formula, so as to facilitate representing the error difference between different weighing sensors based on the difference in the actually measured vehicle weight subsequently.

[0080] Reference Figure 5 , after performing a status detection on the weighing device by comparing the fitted normal distribution parameter values with the target normal distribution parameter values, the above method further includes:

[0081] S41, when the status detection result indicates that the weighing device is in a normal state, mark each first sensor in the first sensor combination and each second sensor in the second sensor combination as a weighing sensor with a normal state;

[0082] S42, when the status detection result indicates that the weighing device is in an abnormal state, determine the sensors to be marked in the first sensor combination and the second sensor combination, where the first sensor combination and the second sensor combination do not include the weighing sensors marked as abnormal, and the sensors to be marked are the weighing sensors that are not marked as normal or abnormal;

[0083] S43, when the number of the determined sensors to be marked is one, mark the determined sensor to be marked as a weighing sensor with an abnormal state; when there are weighing sensors marked as abnormal among multiple weighing sensors, remove the sensor combination that includes the weighing sensor with an abnormal state from the multiple sensor combinations to obtain updated multiple sensor combinations;

[0084] S44, when the number of the determined sensors to be marked is multiple, mark the determined multiple sensors to be marked as a group of sensors with an undetermined state.

[0085] After performing a status detection on the weighing device by comparing the fitted normal distribution parameter values with the target normal distribution parameter values, determine the status detection result of the weighing device, where the status detection result includes that the weighing device is in a normal state and the weighing device is in an abnormal state.

[0086] During the status detection process of the same load-bearing device, in response to the weighing device being in a normal state, mark and record each sensor in the current first sensor combination and the second sensor combination as a normal sensor; in response to the weighing device being in an abnormal state, determine the sensors to be marked in the current first sensor combination and the second sensor combination. Here, the sensors to be marked are the sensors that have not been detected (i.e., the status of the sensors to be marked can be normal or abnormal, the sensors to be marked); when the number of the sensors to be marked is one, determine the sensor to be marked as an abnormal sensor, and when the number of the sensors to be marked is multiple, mark the multiple sensors to be marked as a group of sensors with an undetermined state.

[0087] In some embodiments, in response to the presence of a sensor to be marked being marked as a weighing sensor with an abnormal state, when there is a sensor to be marked among multiple weighing sensors, the sensor combination including the weighing sensor with an abnormal state in the multiple sensor combinations is removed to obtain updated multiple sensor combinations.

[0088] For example, during the state detection process of the same load-bearing device, in response to the weighing device being in a normal state, sensors 1, 2, 3, and 5 in the current first sensor combination and the second sensor combination are marked and recorded as normal sensors; in response to the weighing device being in an abnormal state, at this time, the current first sensor combination and the second sensor combination include sensors 1, 2, 3, 4, and 5. Since sensors 1, 2, 3, and 5 are normal sensors, it is determined that sensor 4 is an abnormal sensor.

[0089] For example, during the state detection process of the same load-bearing device, in response to the weighing device being in a normal state, sensors 1, 2, 3, and 5 in the current first sensor combination and the second sensor combination are marked and recorded as normal sensors; in response to the weighing device being in an abnormal state, at this time, the current first sensor combination and the second sensor combination include sensors 1, 2, 3, 4, 5, and 6. Since sensors 1, 2, 3, and 5 are normal sensors, it is determined that sensors 4 and 6 are a group of sensors with undetermined states.

[0090] Reference Figure 6 , after performing state detection on the weighing device by comparing the fitted normal distribution parameter values and the target normal distribution parameter values, the above method further includes:

[0091] S51, when there is at least one sensor to be marked among multiple weighing sensors, two sensor combinations are reselected from the multiple sensor combinations to obtain a new first sensor combination and a new second sensor combination.

[0092] Wherein, the weighing sensors included in any group of sensors with undetermined states are at least partially different from the weighing sensors included in the new first sensor combination and the new second sensor combination;

[0093] S52, continue to perform the state detection operation based on the new first sensor combination and the new second sensor combination until all multiple weighing sensors have been marked as normal or abnormal weighing sensors.

[0094] To determine the abnormal sensors in the sensor group with undetermined status, two sensor combinations are reselected from multiple sensor combinations (i.e., the new first sensor combination and the new second sensor combination). Here, the reselected sensor combinations include the sensors in the sensor group with undetermined status or sensors in normal status (i.e., only the marked abnormal sensors are excluded), and the new first sensor combination and the new second sensor combination include at least one different sensor. The status detection operation is continued based on the new first sensor combination and the new second sensor combination until all load cells have been marked as normal or abnormal load cells.

[0095] For example, in the case of only 5 sensors, the normal sensors that have been determined are sensor 1 and sensor 2. At this time, when the status detection result is used to indicate that the weighing device is in normal status, the first sensor combination is sensor 1 and sensor 2, and the second sensor combination is sensor 2 and sensor 3, then sensor 3 is determined to be a normal sensor; when the status detection result is used to indicate that the weighing device is in abnormal status, the first sensor combination is sensor 2 and sensor 3, and the second sensor combination is sensor 4 and sensor 5, then sensors 4 and 5 are determined to be sensors with undetermined status; when the status detection result is used to indicate that the weighing device is in abnormal status, the first sensor combination is sensor 1 and sensor 2, and the second sensor combination is sensor 2 and sensor 4, then sensor 4 is determined to be an abnormal sensor. The subsequent process is the same as this logic and will not be elaborated again.

[0096] Through the embodiments provided in this application, by continuously selecting sensor combinations, it is possible to gradually determine some sensors in normal status and exclude some sensors in abnormal status. After subsequent status detection operations, it is possible to determine the detection status of all load cells.

[0097] By comparing the fitted normal distribution parameter values and the target normal distribution parameter values, the weighing device is subjected to status detection to obtain a status detection result, including:

[0098] S61, determine the difference between the parameter values of the same normal distribution parameters in the fitted normal distribution parameter values and the target normal distribution parameter values to obtain a set of parameter differences.

[0099] Among them, each parameter difference in a set of parameter differences corresponds to a parameter difference threshold respectively;

[0100] S62, based on each parameter difference and the parameter difference threshold corresponding to each parameter difference, determine the status detection result;

[0101] Among them, when there is at least one parameter difference greater than or equal to the corresponding parameter difference threshold in a set of parameter differences, the status detection result is used to indicate that the weighing device is in an abnormal state; when each parameter difference is less than the corresponding parameter difference threshold, the status detection result is used to indicate that the weighing device is in a normal state.

[0102] In some embodiments, when the weighing device is implemented as a dynamic vehicle scale, in an actual weighing scenario, after the dynamic vehicle scale is installed at a fixed location, its state is relatively fixed at that location, so its error influence factor is fixed relative to that location. According to the probability distribution principle and the analysis of the detection data, it can be determined that the error distribution at the fixed location conforms to the normal distribution law, as shown in formula (4):

[0103]

[0104] Among them, i represents the i-th sensor combination of the weighing device (i.e., the dynamic vehicle scale), Er(i) represents the measurement error of the i-th sensor group, and u i represents the mean value of the measurement error of the i-th sensor combination, represents the square of the standard deviation of the measurement error.

[0105] In some embodiments, the difference between two errors that conform to the normal distribution (i.e., the error of the first vehicle weight of the first sensor combination and the error of the second measured weight of the second sensor combination) also conforms to the normal distribution, as shown in formula (5):

[0106]

[0107] Among them, i represents the i-th sensor combination of the weighing device (i.e., the dynamic vehicle scale), j represents the j-th sensor combination of the weighing device (i.e., the dynamic vehicle scale), Er(i,j) represents the difference between the measurement error of the i-th sensor group and the measurement error of the j-th sensor group, and u i,j represents the mean value of the difference in measurement errors, represents the square of the standard deviation of the difference in measurement errors.

[0108] Here, in the initial use stage after the weighing device is detected, the vehicle weight measured is the weight under normal sensor conditions. Therefore, the result obtained by fitting the normal distribution with the set of weight differences under normal conditions can be used as a reference standard (i.e., the target normal distribution parameter value), where the target normal distribution parameter value includes the parameter difference threshold corresponding to each parameter difference.

[0109] In some embodiments, Er(i) represents the measurement error of the i-th sensor group and can be expressed by the following formula (6):

[0110] Er(i) = Y(i) - H(i)X(i) (6)

[0111] Among them, Er(i) represents the measurement error of the i-th sensor group, Y(i) represents the actual measured weight of the i-th sensor group, and H(i)X(i) represents the true weight.

[0112] Currently, the difficult point is that the true weight of the vehicle cannot be obtained in real time. The weighing device (dynamic truck scale) outputs the measured weight Y(i). Therefore, in order to eliminate the influence of this problem, the embodiment of the present application adopts the method of error difference, referring to formula (7):

[0113] Er(i,j) = Er(i) - Er(j) = (Y(i) - H(i)X(i)) - (Y(j) - H(j)X(j)) (7)

[0114] Since H(i)X(i) and H(j)X(j) represent the true weight of the vehicle (that is, the true weight of the same vehicle is the same), the error difference can be expressed by formula (8):

[0115] Er(i,j) = Er(i) - Er(j) = Y(i) - Y(j) (8)

[0116] At this time, Y(i) and Y(j) are real-time available values, and Er(i,j) can be represented by the difference in the vehicle weights obtained by two different sensor combinations, so that it can be ensured that each weighing sensor in the weighing device can be monitored in real time.

[0117] For example, first, after the dynamic truck scale is calibrated, the dynamic truck scale is stable in the initial stage of use. At this time, collect the vehicle weight data of different sensor combinations for a certain period of time in the initial stage, and perform normal distribution fitting on the error differences between different sensor combinations to obtain the mean of the target error difference and the square of the standard deviation of the target error difference, and use the mean of the target error difference and the square of the mean of the target error difference as the standard reference value.

[0118] Secondly, use a certain time period as a sliding window, statistically collect the vehicle weight data of different sensor combinations within the window period in real time, and perform normal distribution fitting on the error differences between different sensor combinations to obtain the mean of the real-time error difference and the square of the standard deviation of the real-time error difference, and compare it with the standard reference value. When the detected change exceeds the specified threshold, an alarm is issued.

[0119] Finally, according to the difference in the sensors used between the alarm combination and the normal combination, the position of the specific problem sensor can be accurately located.

[0120] Through the above embodiments provided by the present application, normal distribution fitting is performed using error differences, ensuring that data can be obtained in real time, avoiding the problem of unable to obtain data. By comparing in different combinations, the position of the problem sensor can be accurately located. Automatic real-time monitoring of the device status is achieved, making up for the deficiency of unable to monitor within the cycle; the automatically monitored data can be directly obtained, ensuring the reliability of the data; and the position of the problem sensor can be automatically located, reducing labor costs.

[0121] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0122] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the embodiments of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), including several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present application.

[0123] According to another aspect of the embodiments of the present application, a state detection device for a weighing device is further provided. The device is used to implement the state detection method of the weighing device provided in the above embodiments, and those that have been described will not be repeated. As used below, the terms "module" and "unit" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0124] Figure 7 is a structural block diagram of an optional state detection device for a weighing device according to an embodiment of the present application. As Figure 7 shown, the device includes:

[0125] A selection unit 701 is configured to select a first sensor combination and a second sensor combination from multiple sensor combinations. Each sensor combination in the multiple sensor combinations includes at least one load cell among multiple load cells. Different sensor combinations include at least partially different load cells. Each sensor combination is used to obtain the complete vehicle weight.

[0126] A detection unit 702 is configured to perform the following state detection operations based on the first sensor combination and the second sensor combination: obtain a first vehicle weight of each target vehicle in a set of target vehicles detected by the first sensor combination, and obtain a second vehicle weight of each target vehicle detected by the second sensor combination; perform a normal distribution fitting on a first weight difference corresponding to the set of target vehicles to obtain a fitted normal distribution parameter value, where the first weight difference corresponding to the set of target vehicles is a set of differences between the first vehicle weight of each target vehicle in the set of target vehicles and the second vehicle weight of each target vehicle; perform a state detection on the weighing device by comparing the fitted normal distribution parameter value with a target normal distribution parameter value to obtain a state detection result, where the target normal distribution parameter value is a preset normal distribution parameter value corresponding to the normal distribution satisfied by the set of differences between the vehicle weight detected by the first sensor combination and the vehicle weight detected by the second sensor combination.

[0127] It should be noted that the selection unit 701 in this embodiment can be used to execute the above step S301, and the detection unit 702 in this embodiment can be used to execute the above step S302.

[0128] Through the above steps of the embodiments of the present application, a first sensor combination and a second sensor combination are selected from multiple sensor combinations, where each sensor combination in the multiple sensor combinations includes at least one weighing sensor among multiple weighing sensors, and the weighing sensors included in different sensor combinations are at least partially different, and each sensor combination is used to obtain the complete vehicle weight; the following state detection operations are performed based on the first sensor combination and the second sensor combination: obtaining the first vehicle weight of each target vehicle in a group of target vehicles detected by the first sensor combination, and obtaining the second vehicle weight of each target vehicle detected by the second sensor combination; performing a normal distribution fitting on the first weight differences corresponding to each group of target vehicles to obtain the fitted normal distribution parameter values, where the first weight differences corresponding to a group of target vehicles are a set of differences between the first vehicle weight of each target vehicle in the group of target vehicles and the second vehicle weight of each target vehicle; by comparing the fitted normal distribution parameter values with the target normal distribution parameter values, performing a state detection on the weighing device to obtain a state detection result, where the target normal distribution parameter values are preset normal distribution parameter values corresponding to the normal distribution satisfied by the set of differences between the vehicle weight detected by the first sensor combination and the vehicle weight detected by the second sensor combination, which can improve the accuracy of the state detection result of the weighing device; thereby solving the technical problem of poor real-time performance of the detection method of the weighing device in the related art.

[0129] In an exemplary embodiment, the above device further includes:

[0130] An acquisition unit, configured to, when the state of the weighing device is normal, obtain the first vehicle weight of each reference vehicle in a group of reference vehicles detected by the first sensor combination, and obtain the second vehicle weight of each reference vehicle detected by the second sensor combination;

[0131] A fitting unit, configured to perform a normal distribution fitting on the weight differences corresponding to each reference vehicle to obtain the target normal distribution parameter values.

[0132] In an exemplary embodiment, the selection unit includes:

[0133] A selection module, configured to select a sensor combination pair from a preset multiple sensor combination pairs to obtain a first sensor combination and a second sensor combination, where the multiple sensor combination pairs include two sensor combinations in the multiple sensor combinations, and the weighing sensors included in different sensor combination pairs in the multiple sensor combination pairs are at least partially different.

[0134] In an exemplary embodiment, the detection unit includes:

[0135] A detection module, configured to determine the first vehicle weight of each target vehicle based on the first weight data of each target vehicle detected by each first sensor in the first sensor combination, and determine the second vehicle weight of each target vehicle based on the second weight data of each target vehicle detected by each second sensor in the second sensor combination; wherein, the first weight data of each target vehicle is the weight data detected by each first sensor within a target time period, the second weight data of each target vehicle is the weight data detected by each second sensor within the target time period, and the target time period is the time period between two calibration times for the periodic calibration of the weighing device.

[0136] In an exemplary embodiment, the above device further includes:

[0137] A first marking unit, configured to mark each first sensor in the first sensor combination and each second sensor in the second sensor combination as normal weighing sensors when the status detection result is used to indicate that the status of the weighing device is normal;

[0138] A determination unit, configured to determine the sensors to be marked in the first sensor combination and the second sensor combination when the status detection result is used to indicate that the status of the weighing device is abnormal, wherein the first sensor combination and the second sensor combination do not include weighing sensors marked as abnormal, and the sensors to be marked are weighing sensors not marked as normal or abnormal;

[0139] A second marking unit, configured to mark the determined sensor to be marked as an abnormal weighing sensor when the number of determined sensors to be marked is one; when there are weighing sensors marked as abnormal among multiple weighing sensors, remove the sensor combination including the abnormal weighing sensor from the multiple sensor combinations to obtain updated multiple sensor combinations;

[0140] A third marking unit, configured to mark the determined multiple sensors to be marked as a group of sensors with undetermined status when the number of determined sensors to be marked is multiple.

[0141] In an exemplary embodiment, the above device further includes:

[0142] A re-selection unit, configured to re-select two sensor combinations from the multiple sensor combinations to obtain a new first sensor combination and a new second sensor combination when there is at least one sensor to be marked among the multiple weighing sensors, wherein at least part of the weighing sensors included in any group of sensors with undetermined status is different from the weighing sensors included in the new first sensor combination and the new second sensor combination;

[0143] An execution unit is configured to continue performing a status detection operation based on a new first sensor combination and a new second sensor combination until multiple weighing sensors have all been marked as having normal status or abnormal status.

[0144] In one exemplary embodiment, the detection unit includes:

[0145] A first determination module is configured to determine a difference between parameter values of the same normal distribution parameter in the fitted normal distribution parameter values and the target normal distribution parameter values, so as to obtain a set of parameter differences, where each parameter difference in the set of parameter differences corresponds to a parameter difference threshold respectively;

[0146] A second determination module is configured to determine a status detection result based on each parameter difference and the parameter difference threshold corresponding to each parameter difference; where, in a case where there is at least one parameter difference greater than or equal to the corresponding parameter difference threshold in the set of parameter differences, the status detection result is used to indicate that the status of the weighing device is abnormal; in a case where each parameter difference is less than the corresponding parameter difference threshold, the status detection result is used to indicate that the status of the weighing device is normal.

[0147] It should be noted that the above-mentioned various modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited thereto: the above-mentioned modules are all located in the same processor; or, the above-mentioned various modules are respectively located in different processors in any combination form.

[0148] According to another aspect of the embodiments of the present application, there is also provided a computer-readable storage medium, in which a computer program is stored, where the computer program is configured to execute the steps in any one of the above method embodiments when running.

[0149] In one exemplary embodiment, the above-mentioned computer-readable storage medium may include, but is not limited to: a USB flash drive, a read-only memory (ROM for short), a random access memory (RAM for short), a mobile hard disk, a magnetic disk, or an optical disc, etc., various media that can store computer programs.

[0150] According to one aspect of the present application, there is provided a computer program product, which includes a computer program / instructions, and the computer program / instructions include program codes for executing the method shown in the flowchart. In such an embodiment, refer to Figure 8, the computer program can be downloaded and installed from a network through the communication section 809, and / or installed from the removable medium 811. When the computer program is executed by the central processing unit 801, various functions provided by the embodiments of the present application are executed. The serial numbers of the embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0151] Reference Figure 8 , Figure 8 is a block diagram of the structure of a computer system of an optional electronic device according to an embodiment of the present application.

[0152] Figure 8 Schematically shows a block diagram of the structure of a computer system of an electronic device for implementing the embodiments of the present application. As Figure 8 shown, the computer system 800 includes a central processing unit 801 (Central Processing Unit, CPU), which can perform various appropriate actions and processes according to a program stored in the read-only memory 802 (Read-Only Memory, ROM) or a program loaded from the storage section 808 into the 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 the bus 804. The input / output interface 805 (Input / Output interface, i.e., I / O interface) is also connected to the bus 804.

[0153] The following components are connected to the input / output interface 805: an input section 806 including a keyboard, a mouse, etc.; an output section 807 including, for example, a cathode ray tube (Cathode Ray Tube, CRT), a liquid crystal display (Liquid Crystal Display, LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a local area network card, a modem, etc. The communication section 809 performs communication processing via a network such as the Internet. 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, etc., is installed on the drive 810 as needed so that a computer program read from it can be installed into the storage section 808 as needed.

[0154] In particular, according to an embodiment of the present application, the processes described in each method flow chart can be implemented as computer software programs. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the methods shown in the flow charts. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 809, and / or installed from the removable medium 811. When the computer program is executed by the central processing unit 801, various functions defined in the system of the present application are executed.

[0155] It should be noted that Figure 8 The computer system 800 of the electronic device shown is only an example, and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0156] According to another aspect of the embodiments of the present application, an electronic device is further provided, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0157] In an exemplary embodiment, the above electronic device may further include a transmission device and an input / output device. Among them, the transmission device is connected to the above input / output resource pool, and the input / output device is connected to the above input / output resource pool.

[0158] For the specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary embodiments, and details are not repeated here.

[0159] Obviously, those skilled in the art should understand that the above modules or steps of the embodiments of the present application can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. They can be implemented by program codes executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a different order from here, or they can be separately made into individual integrated circuit modules, or multiple modules or steps among them can be made into a single integrated circuit module for implementation. In this way, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0160] The above are only the preferred embodiments of the present application and are not used to limit the embodiments of the present application. For those skilled in the art, the embodiments of the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the principle of the embodiments of the present application should be included in the protection scope of the embodiments of the present application.

Claims

1. A method for detecting the state of a weighing device, characterized in that, The weighing device includes a plurality of weighing sensors, including: Selecting a first sensor combination and a second sensor combination from a plurality of sensor combinations, wherein each sensor combination in the plurality of sensor combinations includes at least one weighing sensor in the plurality of weighing sensors, and the weighing sensors included in different sensor combinations are at least partially different, and each sensor combination is used to obtain the complete vehicle weight; Performing the following state detection operations based on the first sensor combination and the second sensor combination: Obtaining a first vehicle weight of each target vehicle in a group of target vehicles detected by the first sensor combination, and obtaining a second vehicle weight of each target vehicle detected by the second sensor combination; Performing a normal distribution fitting on the first weight differences corresponding to the group of target vehicles to obtain the fitted normal distribution parameter values, wherein the first weight differences corresponding to the group of target vehicles are the difference sets between the first vehicle weights of each target vehicle in the group of target vehicles and the second vehicle weights of each target vehicle; Performing a state detection on the weighing device by comparing the fitted normal distribution parameter values with target normal distribution parameter values to obtain a state detection result, wherein the target normal distribution parameter values are the normal distribution parameter values preset and corresponding to the normal distribution satisfied by the difference sets between the vehicle weights detected by the first sensor combination and the vehicle weights detected by the second sensor combination; 2. The method according to claim 1, wherein Before selecting the first sensor combination and the second sensor combination from the plurality of sensor combinations, the method further includes: When the weighing device is in a normal state, obtaining a first vehicle weight of each reference vehicle in a group of reference vehicles detected by the first sensor combination, and obtaining a second vehicle weight of each reference vehicle detected by the second sensor combination; Performing a normal distribution fitting on the weight differences corresponding to each reference vehicle to obtain the target normal distribution parameter values.

3. The method according to claim 1, characterized in that The selecting the first sensor combination and the second sensor combination from the plurality of sensor combinations includes: Selecting a pair of sensor combinations from a preset plurality of pairs of sensor combinations to obtain the first sensor combination and the second sensor combination, wherein the plurality of pairs of sensor combinations includes two sensor combinations in the plurality of sensor combinations, and the weighing sensors included in different pairs of sensor combinations in the plurality of pairs of sensor combinations are at least partially different.

4. The method according to claim 1, wherein The obtaining a first vehicle weight of each target vehicle in a group of target vehicles detected by the first sensor combination, and obtaining a second vehicle weight of each target vehicle detected by the second sensor combination includes: Determine the first vehicle weight of each target vehicle based on the first weight data of each target vehicle detected by each first sensor in the first sensor combination, and determine the second vehicle weight of each target vehicle based on the second weight data of each target vehicle detected by each second sensor in the second sensor combination; Wherein, the first weight data of each target vehicle are all weight data detected by each first sensor within a target time period, the second weight data of each target vehicle are all weight data detected by each second sensor within the target time period, and the target time period is the time period between two calibration times for performing periodic calibration on the weighing device.

5. The method according to claim 1, wherein After performing the status detection on the weighing device by comparing the fitted normal distribution parameter values with the target normal distribution parameter values, the method further includes: When the status detection result is used to indicate that the status of the weighing device is normal, mark each first sensor in the first sensor combination and each second sensor in the second sensor combination as weighing sensors with normal status; When the status detection result is used to indicate that the status of the weighing device is abnormal, determine the sensors to be marked in the first sensor combination and the second sensor combination, wherein the first sensor combination and the second sensor combination do not include weighing sensors marked as having abnormal status, and the sensors to be marked are weighing sensors that are not marked as having normal status or abnormal status; When the number of the determined sensors to be marked is one, mark the determined sensor to be marked as a weighing sensor with abnormal status; when there are weighing sensors marked as having abnormal status among the multiple weighing sensors, remove the sensor combination containing the weighing sensor with abnormal status from the multiple sensor combinations to obtain the updated multiple sensor combinations; When the number of the determined sensors to be marked is multiple, mark the determined multiple sensors to be marked as a group of sensors with undetermined status.

6. The method according to claim 5, wherein After performing the status detection on the weighing device by comparing the fitted normal distribution parameter values with the target normal distribution parameter values, the method further includes: When there is at least one of the sensors to be marked among the multiple weighing sensors, re-select two sensor combinations from the multiple sensor combinations to obtain a new first sensor combination and a new second sensor combination, wherein at least some of the weighing sensors included in any group of sensors with undetermined status are different from the weighing sensors included in the new first sensor combination and the new second sensor combinations; Continue to perform the status detection operation based on the new first sensor combination and the new second sensor combination until all the multiple weighing sensors have been marked as weighing sensors with normal status or abnormal status.

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

8. A state detection device for a weighing device, characterized in that, Including: A selection unit for selecting a first sensor combination and a second sensor combination from multiple sensor combinations, where each sensor combination in the multiple sensor combinations includes at least one weighing sensor among the multiple weighing sensors, and different sensor combinations include at least partially different weighing sensors, and each sensor combination is used to obtain the complete vehicle weight; A detection unit for performing the following state detection operations based on the first sensor combination and the second sensor combination: Obtaining the first vehicle weight of each target vehicle in a set of target vehicles detected by the first sensor combination, and obtaining the second vehicle weight of each target vehicle detected by the second sensor combination; Performing normal distribution fitting on the first weight difference corresponding to the set of target vehicles to obtain the fitted normal distribution parameter values, where the first weight difference corresponding to the set of target vehicles is a set of differences between the first vehicle weight and the second vehicle weight of each target vehicle in the set of target vehicles; Performing state detection on the weighing device by comparing the fitted normal distribution parameter values with the target normal distribution parameter values to obtain a state detection result, where the target normal distribution parameter values are the normal distribution parameter values preset and corresponding to the normal distribution satisfied by the set of differences between the vehicle weight detected by the first sensor combination and the vehicle weight detected by the second sensor combination.

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

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

Images