Overload control data quality evaluation method, medium and system

Abstract

The present invention discloses a method, medium and system for evaluating the quality of overweight control data, wherein the method comprises: obtaining actual indicator data corresponding to a plurality of measurement indicators; classifying the actual indicator data to obtain a plurality of data indicators, and calculating the indicator score corresponding to each of the data indicators; constructing a hierarchical structure model using the hierarchical analysis method, and determining the weight value corresponding to each data indicator through the hierarchical structure model and the knowledge of industry experts; and calculating the data quality score corresponding to the actual indicator data according to the indicator score and the weight value. The method can effectively improve the efficiency of the quality evaluation of overweight control data; at the same time, improve the accuracy and reference of the quality evaluation of overweight control data.

Description

Technical Field

[0001] The present application relates to the technical field of big data analysis, and in particular to a method, medium and system for evaluating the quality of overweight control data. Background Art

[0002] With the rapid economic growth and the booming logistics industry, the volume of freight has increased significantly, which has brought unprecedented pressure to the work of overweight control.

[0003] In the related art, when evaluating the quality of overweight control data, it is often done through manual screening. In other words, the overweight control data is manually checked to judge the quality of the overweight control data based on manual experience. However, this method is not only inefficient; but also, due to human uncertainty and the uneven, incomplete, inaccurate, and redundant quality of overweight control data, it is easy to lead to inaccurate final evaluation results. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, one purpose of the present invention is to propose a method for evaluating the quality of overweight control data, which can effectively improve the efficiency of the quality evaluation of overweight control data; at the same time, improve the accuracy and reference of the quality evaluation of overweight control data.

[0005] In the first aspect, an embodiment of the present invention proposes a method for evaluating the quality of overweight vehicle control data, including: obtaining actual indicator data corresponding to multiple measurement indicators; classifying the actual indicator data to obtain multiple data indicators, and calculating the indicator score corresponding to each of the data indicators; using the hierarchical analysis method to construct a hierarchical structure model, and determining the weight value corresponding to each data indicator through the hierarchical structure model and industry expert knowledge; calculating the data quality score corresponding to the actual indicator data according to the indicator score and the weight value.

[0006] According to the method for evaluating the quality of overweight vehicle control data in an embodiment of the present invention, first, actual indicator data corresponding to a plurality of measurement indicators are obtained; then, the actual indicator data are classified to obtain a plurality of data indicators, and the indicator score corresponding to each of the data indicators is calculated; then, a hierarchical structure model is constructed using the analytic hierarchy process, and the weight value corresponding to each data indicator is determined through the hierarchical structure model and the knowledge of industry experts; then, the data quality score corresponding to the actual indicator data is calculated according to the indicator score and the weight value. Thereby, the efficiency of the quality evaluation of overweight vehicle control data is effectively improved; at the same time, the accuracy and referenceability of the quality evaluation of overweight vehicle control data are improved.

[0007] In some embodiments, the multiple data indicators include primary indicators and secondary indicators corresponding to the primary indicators, wherein the primary indicators include data integrity indicators, data consistency indicators, data accuracy indicators and data validity indicators; the secondary indicators corresponding to the data consistency indicators include speed consistency indicators, axle number consistency indicators, vehicle model consistency indicators and vehicle quantity consistency indicators; the secondary indicators corresponding to the data accuracy indicators include low-speed driving behavior indicators, speeding driving behavior indicators, parking driving behavior indicators, edge-pressing driving behavior indicators, lane-crossing driving behavior indicators, reverse driving behavior indicators, scale-grinding driving behavior indicators, scale-jumping driving behavior indicators, close-following vehicle behavior indicators and environmental metering status indicators; the secondary indicators corresponding to the data validity indicators include multi-axle vehicle indicators, broken axle vehicle indicators, no upper scale signal indicators and no lower scale signal indicators.

[0008] In some embodiments, the indicator score corresponding to the data integrity indicator is calculated by the following formula:

[0009]

[0010]

[0011] in, Indicates the indicator score corresponding to the data integrity indicator, Indicates the total number of devices measuring the indicator, Indicates An indicator measures the effectiveness of the equipment.

[0012] In some embodiments, the index score corresponding to the data consistency index is calculated by the following formula:

[0013]

[0014] in, Indicates the indicator score corresponding to the data consistency indicator, Indicates the index score corresponding to the speed consistency index, Indicates the index score corresponding to the axis number consistency index, Indicates the index score corresponding to the vehicle model consistency index, Indicates the indicator score corresponding to the vehicle volume consistency indicator;

[0015]

[0016]

[0017]

[0018]

[0019] in, Indicates the car speed value measured by the weighing equipment. Indicates the car speed value measured by the radar device. Represents the car speed value measured by the camera device. Indicates the average speed of the car. express , , The maximum value in express , , The minimum value in ;

[0020]

[0021] in, Indicates the index score corresponding to the axis number consistency index, Indicates the number of vehicle axles measured by the weighing equipment. Indicates the number of vehicle axles measured by the radar device, Indicates the number of vehicle axes measured by the camera device;

[0022]

[0023] in, Indicates the index score corresponding to the vehicle model consistency index, Indicates the car model data measured by the radar equipment, Represents the car model data measured by the camera device;

[0024]

[0025] in, Indicates the indicator score corresponding to the vehicle volume consistency indicator, Indicates the number of cars measured by the weighing equipment. Represents the number of cars measured by the camera device. Indicates the number of cars measured by the radar device.

[0026] In some embodiments, the indicator score corresponding to the data accuracy indicator is calculated by the following formula:

[0027]

[0028] in, Indicates the indicator score corresponding to the data accuracy indicator, Indicates The indicator score of the secondary indicator corresponding to each data accuracy indicator.

[0029] In some embodiments, the index score corresponding to the multi-axle vehicle index or the index score corresponding to the broken axle vehicle index is calculated by the following formula:

[0030]

[0031] in, Indicates the index score corresponding to the multi-axle vehicle index, Indicates the index score corresponding to the broken axle vehicle index, Indicates the axis number data that is corrected after the system detects abnormal behavior. Indicates the axis number data measured by the radar device. Indicates the axis number data measured by the camera device;

[0032] The indicator score corresponding to the no-upper-name signal indicator or the indicator score corresponding to the no-lower-name signal indicator is calculated by the following formula:

[0033]

[0034] in, Indicates the indicator score corresponding to the supreme signal indicator, There is no indicator score corresponding to the signal indicator below. Indicates the total number of devices measuring the indicator, Indicates An indicator measures the effectiveness of the equipment.

[0035] In some embodiments, a hierarchical structure model is constructed using the hierarchical analysis method, and a weight value corresponding to each data indicator is determined using the hierarchical structure model and industry expert knowledge, including:

[0036] A hierarchical structure model is constructed by using the hierarchical analysis method, wherein the hierarchical structure model includes a target layer, a criterion layer and a sub-criterion layer, the criterion layer includes a data integrity index, a data consistency index, a data accuracy index and a data validity index, and the sub-criterion layer includes secondary indicators corresponding to each indicator in the criterion layer;

[0037] A judgment matrix is ​​constructed based on the industry expert knowledge, and a weight value corresponding to each data indicator is calculated according to the judgment matrix.

[0038] In some embodiments, the method further includes: performing a consistency check on the judgment matrix to obtain a consistency index value and a consistency ratio value corresponding to the judgment matrix; judging whether the consistency index value and the consistency ratio value are valid based on a preset value range; if so, determining that the judgment matrix is ​​valid.

[0039] In a second aspect, an embodiment of the present invention proposes a computer-readable storage medium on which a program for evaluating the quality of overweight vehicle control data is stored. When the program for evaluating the quality of overweight vehicle control data is executed by a processor, the method for evaluating the quality of overweight vehicle control data as described above is implemented.

[0040] In the third aspect, an embodiment of the present invention proposes a system for evaluating the quality of overweight vehicle control data, comprising: a data acquisition module, the data acquisition module is used to obtain actual indicator data corresponding to multiple measurement indicators; a data indicator classification module, the data indicator classification module is used to classify the actual indicator data to obtain multiple data indicators, and calculate the indicator score corresponding to each of the data indicators; an indicator weight calculation module, the indicator weight calculation module is used to construct a hierarchical structure model using the hierarchical analysis method, and determine the weight value corresponding to each data indicator through the hierarchical structure model and industry expert knowledge; a data evaluation module, the data evaluation module is used to calculate the data quality score corresponding to the actual indicator data based on the indicator score and the weight value.

[0041] According to the overweight vehicle control data quality evaluation system of the embodiment of the present invention, a data acquisition module is set to obtain actual indicator data corresponding to a variety of measurement indicators; a data indicator classification module is used to classify the actual indicator data to obtain multiple data indicators, and calculate the indicator score corresponding to each of the data indicators; the indicator weight calculation module is used to construct a hierarchical structure model using the hierarchical analysis method, and determine the weight value corresponding to each data indicator through the hierarchical structure model and industry expert knowledge; the data evaluation module is used to calculate the data quality score corresponding to the actual indicator data according to the indicator score and the weight value; thereby effectively improving the quality evaluation efficiency of overweight vehicle control data; at the same time, improving the accuracy and reference value of overweight vehicle control data quality evaluation.

[0042] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flow chart of a method for evaluating overweight control data quality according to an embodiment of the present invention;

[0044] Figure 2 4 is a block diagram of a system for evaluating overweight control data quality according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0046] The following describes a method for evaluating overweight control data quality according to an embodiment of the present invention with reference to the accompanying drawings.

[0047] See also Figure 1 , Figure 1 According to the method for evaluating the quality of overweight control data according to an embodiment of the present invention, Figure 1 As shown, the overweight control data quality evaluation method includes the following steps:

[0048] S101, obtaining actual indicator data corresponding to a plurality of measurement indicators.

[0049] That is to say, there are multiple indicator measurement devices in the work of overload control, and according to the needs of overload control, there will also be multiple measurement indicators (for example, speeding indicator, speed indicator, axle number indicator, etc.). Therefore, in order to effectively evaluate the quality of overload control data, the actual indicator data corresponding to the multiple measurement indicators are first obtained.

[0050] As an example, data is collected from multiple overweight control points to upload massive time and space data to various perception platforms to form a new Internet of Things data collection module based on full-time and space group intelligence perception; and actual indicator data is obtained through the new Internet of Things data collection module based on full-time and space group intelligence perception.

[0051] S102, classifying the actual indicator data to obtain a plurality of data indicators, and calculating an indicator score corresponding to each data indicator.

[0052] In some embodiments, multiple data indicators include primary indicators and secondary indicators corresponding to the primary indicators, wherein the primary indicators include data integrity indicators, data consistency indicators, data accuracy indicators and data validity indicators; the secondary indicators corresponding to the data consistency indicators include speed consistency indicators, axle number consistency indicators, vehicle model consistency indicators and vehicle quantity consistency indicators; the secondary indicators corresponding to the data accuracy indicators include low-speed driving behavior indicators, speeding driving behavior indicators, parking driving behavior indicators, edge-pressing driving behavior indicators, lane-crossing driving behavior indicators, reverse driving behavior indicators, scale-grinding driving behavior indicators, scale-jumping driving behavior indicators, close-following vehicle behavior indicators and environmental measurement status indicators; the secondary indicators corresponding to the data validity indicators include multi-axle vehicle indicators, broken axle vehicle indicators, no upper scale signal indicators and no lower scale signal indicators.

[0053] In some embodiments, the indicator score corresponding to the data integrity indicator is calculated by the following formula:

[0054]

[0055]

[0056] in, Indicates the indicator score corresponding to the data integrity indicator, Indicates the total number of devices measuring the indicator, Indicates An indicator measures the effectiveness of the equipment.

[0057] In other words, the data integrity index is defined as the ratio of valid data to the corresponding collected data in the real information data that can reflect the measured car at a specific time and space interval. The value range is [0,1]. The larger the value, the higher the integrity.

[0058] As an example, in order to accurately and fully evaluate the data, the system considers the operation of each measuring device during a single measurement and determines its evaluation method. (detect vehicle weight and axle number), radar equipment is (detecting vehicle size data, speed and temperature), the camera device is (collecting vehicle license plate number, license plate color, license plate image, vehicle front image, vehicle side image and vehicle rear image), the hard disk video recorder equipment is (Collect vehicle driving video), coil equipment is (collect vehicle up and down scale signals), environmental measurement sensors are (Collect temperature and humidity data to determine whether weighing is affected by temperature.) Specifically, the camera can be determined according to the lane, with 3 cameras for 1 lane. There are also special cases for radar, where two radars can be set up separately for uplink and downlink, and all other equipment is one.

[0059] In some embodiments, the indicator score corresponding to the data consistency indicator is calculated by the following formula:

[0060]

[0061] in, Indicates the indicator score corresponding to the data consistency indicator, Indicates the index score corresponding to the speed consistency index, Indicates the index score corresponding to the axis number consistency index, Indicates the index score corresponding to the vehicle model consistency index, Indicates the indicator score corresponding to the vehicle volume consistency indicator.

[0062] In other words, the definition of consistency index represents the degree to which different indicator measurement devices are logically consistent when detecting the same data indicator; it is used to evaluate the correlation between indicator measurement devices.

[0063] The indicator score corresponding to the speed consistency indicator is calculated by the following formula:

[0064]

[0065]

[0066]

[0067]

[0068] in, Indicates the car speed value measured by the weighing equipment. Indicates the car speed value measured by the radar device. Represents the car speed value measured by the camera device. Indicates the average speed of the car. express , , The maximum value in express , , The minimum value in .

[0069] In other words, speed consistency is the consistency of the results obtained by different indicator measurement devices for the same data indicator in a single measurement. The value range is [0,1], and the larger the value, the better the speed consistency. Since the speed can be detected by weighing equipment, radar equipment and camera equipment at the same time, it is necessary to cross-compare the three parties.

[0070] The score corresponding to the axis number consistency index is calculated by the following formula:

[0071]

[0072] in, Indicates the index score corresponding to the axis number consistency index, Indicates the number of vehicle axles measured by the weighing equipment. Indicates the number of vehicle axles measured by the radar device, Indicates the number of vehicle axes measured by the camera device.

[0073] In other words, the axis number consistency index is defined as the deviation of the results obtained by different indicator measurement devices for the same data indicator in a single measurement. The value is 0 or 1, 0 means that there are inconsistent devices, and 1 means that the measurement results of the axis number of each device are consistent.

[0074] The index score corresponding to the vehicle model consistency index is calculated using the following formula:

[0075]

[0076] in, Indicates the index score corresponding to the vehicle model consistency index, Indicates the car model data measured by the radar equipment, Represents the car model data measured by the camera device.

[0077] In other words, the vehicle model consistency index is defined as the deviation of the results obtained by different indicator measurement devices for the same data indicator in a single measurement. The value is 0 or 1. 0 indicates that inconsistent devices appear, and 1 indicates that the measurement results of the vehicle model by each device are consistent.

[0078] The indicator score corresponding to the vehicle volume consistency indicator is calculated using the following formula:

[0079]

[0080] in, Indicates the indicator score corresponding to the vehicle volume consistency indicator, Indicates the number of cars measured by the weighing equipment. Represents the number of cars measured by the camera device. Indicates the number of cars measured by the radar device.

[0081] In other words, the vehicle quantity consistency index is defined as the deviation of the results of different indicator measurement devices measuring the number of cars within the same measurement time range under a single measurement. The value is 0 or 1, 0 means there are inconsistent devices, and 1 means that the measurement results of the vehicle quantity by each device are consistent.

[0082] In some embodiments, the indicator score corresponding to the data accuracy indicator is calculated by the following formula:

[0083]

[0084] in, Indicates the indicator score corresponding to the data accuracy indicator, Indicates The indicator score of the secondary indicator corresponding to each data accuracy indicator.

[0085] As an example, the definition of data accuracy evaluation indicators is used to judge the closeness between the data collection value and the true value or whether the collection value accurately reflects the real world scene. Since operations such as driving behavior may cause deviations between the collection value and the true value, the low-speed driving behavior indicator ( ), speeding behavior indicators ( )、Parking driving behavior index( )、Border-pressing driving behavior index( )、Cross-lane driving behavior index( ), reverse driving behavior index ( )、Mocheng driving behavior index( )、Jumping driving behavior index( ), close following behavior indicators ( ) and environmental measurement status indicators ( ) to evaluate the accuracy of weighing data. Once the system detects these driving behaviors, the corresponding output is 0, otherwise it is 1.

[0086] In some embodiments, the index score corresponding to the multi-axle vehicle index or the index score corresponding to the broken axle vehicle index is calculated by the following formula:

[0087]

[0088] in, Indicates the index score corresponding to the multi-axle vehicle index, Indicates the index score corresponding to the broken axle vehicle index, Indicates the axis number data that is corrected after the system detects abnormal behavior. Indicates the axis number data measured by the radar device. Indicates the axis number data measured by the camera device;

[0089] The indicator score corresponding to the indicator without the upper signal or the indicator score corresponding to the indicator without the lower signal is calculated by the following formula:

[0090]

[0091] in, Indicates the indicator score corresponding to the supreme signal indicator, There is no indicator score corresponding to the signal indicator below. Indicates the total number of devices measuring the indicator, Indicates An indicator measures the effectiveness of the equipment.

[0092] In other words, the definition of data validity indicators is used to judge whether the data meets certain validity criteria or meets the acceptance conditions. It is usually due to abnormal situations caused by indirect and direct problems of system equipment. Therefore, the definition of multi-axle vehicle indicators ( )、Broken axle vehicle index( ), no signal indicator ( ) and the Supreme Signal Indicator ( ) Four abnormal situations are used to comprehensively evaluate the validity of the data.

[0093] S103, constructing a hierarchical structure model using the hierarchical analysis method, and determining the weight value corresponding to each data indicator through the hierarchical structure model and industry expert knowledge.

[0094] In some embodiments, a hierarchical structure model is constructed using the analytic hierarchy process, and the weight value corresponding to each data indicator is determined through the hierarchical structure model and industry expert knowledge, including: constructing a hierarchical structure model using the analytic hierarchy process, wherein the hierarchical structure model includes a target layer, a criterion layer and a sub-criterion layer, the criterion layer includes data integrity indicators, data consistency indicators, data accuracy indicators and data validity indicators, and the sub-criterion layer includes secondary indicators corresponding to each indicator in the criterion layer; constructing a judgment matrix based on industry expert knowledge, and calculating the weight value corresponding to each data indicator according to the judgment matrix.

[0095] In some embodiments, the evaluation method includes: performing a consistency check on the judgment matrix to obtain a consistency index value and a consistency ratio value corresponding to the judgment matrix; judging whether the consistency index value and the consistency ratio value are valid based on a preset value range; if so, determining that the judgment matrix is ​​valid.

[0096] As an example, the hierarchical structure model is first divided into three levels: the target layer (i.e., data quality score), the criterion layer (including data integrity indicators, data consistency indicators, data accuracy indicators, and data validity indicators), and the sub-criterion layer (secondary indicators corresponding to data integrity indicators, secondary indicators corresponding to data consistency indicators, secondary indicators corresponding to data accuracy indicators, and secondary indicators corresponding to data validity indicators); then, in order to avoid the subjectivity of weight assignment and information quantification problems in AHP, the judgment matrix is ​​constructed through the knowledge of industry experts. Specifically, multiple experts compare each criterion and sub-criteria in pairs to comprehensively construct the judgment matrix. The elements of the judgment matrix represent the importance of one factor relative to another.

[0097] As an example, for the criteria layer , the judgment matrix can be constructed as:

[0098]

[0099] Next, the weight vector corresponding to each data indicator is calculated through the judgment matrix; that is, the weight vector is calculated using the eigenvector method or the normalization method. The calculation process can be expressed by the following formula:

[0100]

[0101] Then, the judgment matrix is ​​checked for consistency to ensure the consistency of multiple expert judgments. If the consistency index value and the consistency ratio value are within the preset value range, it means that the judgment matrix is ​​valid.

[0102] Specifically, first, after obtaining the weight matrix, the maximum eigenvalue is calculated, which is expressed by the following formula:

[0103]

[0104] The consistency index value is expressed by the following formula:

[0105]

[0106] By querying Table 1, the value of the average random consistency index can be obtained.

[0107] Table 1. Random consistency index query table

[0108]

[0109] Next, the corresponding consistency ratio value can be calculated based on the average random consistency index value and the consistency index value:

[0110]

[0111] In this way, when the consistency index value and the consistency ratio value meet the requirements, the weight value corresponding to each data index can be calculated according to the above method.

[0112] S104, calculating the data quality score corresponding to the actual indicator data according to the indicator score and the weight value.

[0113] As a specific embodiment of the present invention, first, for the criterion layer , the judgment matrix can be constructed as:

[0114]

[0115] Next, the judgment matrix is ​​constructed through the subjective judgment of multiple experts. Specifically, the judgment matrix is ​​shown in Table 2:

[0116] Table 2. Judgment matrix example 1

[0117]

[0118] It can be understood that the values ​​in the judgment matrix can represent the importance of one factor relative to another factor, usually using a scale of 1 to 9 to represent relative importance, where:

[0119] 1 means both factors are equally important.

[0120] 3 means one factor is slightly more important than the other.

[0121] 5 means one factor is significantly more important than the other.

[0122] 7 means one factor is more important than the other.

[0123] 9 means one factor is extremely more important than the other.

[0124] 2, 4, 6, 8 represent the middle values ​​of the above adjacent judgments.

[0125] The reverse judgment uses the reciprocal, for example, if A is more important than B and its value is 3, then the value of B's ​​importance to A is 1 / 3.

[0126] Then, the judgment matrix is ​​divided into columns, and each element is divided by the sum of the column, and Table 3 is obtained:

[0127] Table 3. Judgment matrix example 2

[0128]

[0129] Next, add the normalized columns and sum them by rows to get:

[0130] Table 4. Normalized summation data table

[0131]

[0132] Divide each added element by n to get the corresponding weight vector:

[0133] Table 5. Weight vector data table

[0134]

[0135] It can be seen that the original matrix is ​​shown in Table 2.

[0136] The normalized weights are:

[0137] Table 6. Standardized weight information table

[0138]

[0139] in this way, We can get:

[0140]

[0141] Then, according to the calculation formula of the maximum characteristic root:

[0142]

[0143] Can be obtained is 4.045667136.

[0144] Next, according to the calculation formula of the consistency index value:

[0145]

[0146] Can be obtained .

[0147] From Table 1, we can see that the value of the average random consistency index is 0.89.

[0148] Then, according to the calculation formula of the consistency ratio value:

[0149]

[0150] Available .

[0151] Then, it is assumed that the preset value intervals of the consistency index value and the consistency ratio value are both less than 0.1. From the above, it can be seen that both values ​​are less than 0.1, indicating that the judgment matrix is ​​valid.

[0152] Then, after checking the indicators, the weights of each first-level indicator can be determined, that is, the full scores of data integrity, data consistency, data accuracy and data validity are 48.83, 21.16, 9.07 and 20.94 respectively. In this way, the scoring method of each indicator in the criterion layer can be determined according to the above method to conduct a comprehensive evaluation method for overweight control data, which can be expressed by the following formula:

[0153]

[0154] In summary, according to the method for evaluating the quality of overweight vehicle control data in an embodiment of the present invention, first, actual indicator data corresponding to a plurality of measurement indicators are obtained; then, the actual indicator data are classified to obtain a plurality of data indicators, and the indicator score corresponding to each of the data indicators is calculated; then, a hierarchical structure model is constructed using the analytic hierarchy process, and the weight value corresponding to each data indicator is determined through the hierarchical structure model and the knowledge of industry experts; then, the data quality score corresponding to the actual indicator data is calculated according to the indicator score and the weight value. Thereby, the efficiency of the quality evaluation of overweight vehicle control data is effectively improved; at the same time, the accuracy and referenceability of the quality evaluation of overweight vehicle control data are improved.

[0155] In a second aspect, an embodiment of the present invention proposes a computer-readable storage medium on which a program for evaluating the quality of overweight vehicle control data is stored. When the program for evaluating the quality of overweight vehicle control data is executed by a processor, the method for evaluating the quality of overweight vehicle control data as described above is implemented.

[0156] In a third aspect, an embodiment of the present invention provides a system for evaluating the quality of overweight control data, such as Figure 2 As shown, the overweight control data quality evaluation system includes: a data acquisition module 10, a data indicator classification module 20, an indicator weight calculation module 30 and a data evaluation module 40.

[0157] The data acquisition module 10 is used to obtain actual indicator data corresponding to various measurement indicators;

[0158] The data indicator classification module 20 is used to classify the actual indicator data to obtain multiple data indicators and calculate the indicator score corresponding to each of the data indicators;

[0159] The indicator weight calculation module 30 is used to construct a hierarchical structure model using the hierarchical analysis method, and determine the weight value corresponding to each data indicator through the hierarchical structure model and industry expert knowledge;

[0160] The data evaluation module 40 is used to calculate the data quality score corresponding to the actual indicator data according to the indicator score and the weight value.

[0161] It should be noted that the above description of the overweight control data quality evaluation method is also applicable to the overweight control data quality evaluation system and will not be repeated here.

[0162] In summary, according to the overweight vehicle control data quality evaluation system of the embodiment of the present invention, a data acquisition module is set to obtain actual indicator data corresponding to multiple measurement indicators; a data indicator classification module is used to classify the actual indicator data to obtain multiple data indicators, and calculate the indicator score corresponding to each of the data indicators; the indicator weight calculation module is used to construct a hierarchical structure model using the hierarchical analysis method, and determine the weight value corresponding to each data indicator through the hierarchical structure model and industry expert knowledge; the data evaluation module is used to calculate the data quality score corresponding to the actual indicator data according to the indicator score and the weight value; thereby effectively improving the quality evaluation efficiency of overweight vehicle control data; at the same time, improving the accuracy and reference value of overweight vehicle control data quality evaluation.

[0163] It should be noted that the logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0164] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0165] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0166] In the description of the present invention, it is to be understood that the terms “center”, “longitudinal”, “lateral”, “length”, “width”, “thickness”, “up”, “down”, “front”, “back”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inside”, “outside”, “clockwise”, “counterclockwise”, “axial”, “radial”, “circumferential”, etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0167] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0168] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0169] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0170] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating the quality of overweight vehicle control data, characterized in that: The following steps are involved: Obtain actual indicator data corresponding to multiple measurement indicators; Classifying the actual indicator data to obtain a plurality of data indicators, and calculating an indicator score corresponding to each of the data indicators; Using the analytic hierarchy process to construct a hierarchical structure model, and determining the weight value corresponding to each data indicator through the hierarchical structure model and industry expert knowledge; Calculate the data quality score corresponding to the actual indicator data according to the indicator score and the weight value; The multiple data indicators include primary indicators and secondary indicators corresponding to the primary indicators; Among them, the first-level indicators include data integrity indicators, data consistency indicators, data accuracy indicators and data validity indicators; The secondary indicators corresponding to the data consistency index include speed consistency index, axle number consistency index, vehicle type consistency index and vehicle quantity consistency index; The secondary indicators corresponding to the data accuracy indicators include low-speed driving behavior indicators, speeding driving behavior indicators, parking driving behavior indicators, edge-pressing driving behavior indicators, lane-crossing driving behavior indicators, reverse driving behavior indicators, grinding driving behavior indicators, jumping driving behavior indicators, close following behavior indicators and environmental measurement status indicators; The secondary indicators corresponding to the data validity indicators include a multi-axle vehicle indicator, a broken axle vehicle indicator, a no upper scale signal indicator, and a no lower scale signal indicator; The indicator score corresponding to the data consistency indicator is calculated by the following formula: in, Indicates the indicator score corresponding to the data consistency indicator, Indicates the indicator score corresponding to the speed consistency indicator, Indicates the index score corresponding to the axis number consistency index, Indicates the index score corresponding to the vehicle model consistency index, Indicates the indicator score corresponding to the vehicle volume consistency indicator; in, Indicates the car speed value measured by the weighing equipment. Indicates the car speed value measured by the radar device. Represents the car speed value measured by the camera device. represents the average speed of the car, express , , The maximum value in express , , The minimum value in ; in, Indicates the index score corresponding to the axis number consistency index, Indicates the number of vehicle axles measured by the weighing equipment. Indicates the number of vehicle axles measured by the radar device, Indicates the number of vehicle axes measured by the camera device; in, Indicates the index score corresponding to the vehicle model consistency index, Indicates the car model data measured by the radar equipment, Represents the car model data measured by the camera device; in, Indicates the indicator score corresponding to the vehicle volume consistency indicator, Indicates the number of cars measured by the weighing equipment. Represents the number of cars measured by the camera device. Indicates the number of cars measured by the radar device.

2. The method for evaluating overweight vehicle control data quality according to claim 1, characterized in that: The indicator score corresponding to the data integrity indicator is calculated by the following formula: in, Indicates the indicator score corresponding to the data integrity indicator, Indicates the total number of devices measuring the indicator, Indicates An indicator measures the effectiveness of the equipment.

3. The method for evaluating overweight vehicle control data quality according to claim 1, characterized in that: The indicator score corresponding to the data accuracy indicator is calculated by the following formula: in, Indicates the indicator score corresponding to the data accuracy indicator, Indicates The indicator score of the secondary indicator corresponding to each data accuracy indicator.

4. The method for evaluating overweight vehicle control data quality according to claim 1, characterized in that: The index score corresponding to the multi-axle vehicle index or the index score corresponding to the broken axle vehicle index is calculated by the following formula: in, Indicates the index score corresponding to the multi-axle vehicle index, Indicates the index score corresponding to the broken axle vehicle index, Indicates the axis number data that is corrected after the system detects abnormal behavior. Indicates the axis number data measured by the radar device. Indicates the axis number data measured by the camera device; The indicator score corresponding to the no-upper-name signal indicator or the indicator score corresponding to the no-lower-name signal indicator is calculated by the following formula: in, Indicates the indicator score corresponding to the supreme signal indicator, There is no indicator score corresponding to the signal indicator below. Indicates the total number of devices measuring the indicator, Indicates An indicator measures the effectiveness of the equipment.

5. The method for evaluating overweight vehicle control data quality according to claim 1, characterized in that: A hierarchical structure model is constructed using the hierarchical analysis method, and the weight value corresponding to each data indicator is determined through the hierarchical structure model and industry expert knowledge, including: A hierarchical structure model is constructed by using the hierarchical analysis method, wherein the hierarchical structure model includes a target layer, a criterion layer and a sub-criterion layer, the criterion layer includes a data integrity index, a data consistency index, a data accuracy index and a data validity index, and the sub-criterion layer includes secondary indicators corresponding to each indicator in the criterion layer; A judgment matrix is ​​constructed based on the industry expert knowledge, and a weight value corresponding to each data indicator is calculated according to the judgment matrix.

6. The method for evaluating overweight vehicle control data quality according to claim 5, characterized in that: Also includes: Performing a consistency check on the judgment matrix to obtain a consistency index value and a consistency ratio value corresponding to the judgment matrix; Determine whether the consistency index value and the consistency ratio value are valid based on a preset value interval; If yes, it is determined that the judgment matrix is ​​valid.

7. A computer-readable storage medium, characterized in that: An overweight control data quality evaluation program is stored thereon, and when the overweight control data quality evaluation program is executed by the processor, the overweight control data quality evaluation method as described in any one of claims 1-6 is implemented.

8. A system for evaluating the quality of overweight vehicle control data, characterized in that: include: A data acquisition module, wherein the data acquisition module is used to obtain actual indicator data corresponding to a plurality of measurement indicators; A data indicator classification module, wherein the data indicator classification module is used to classify the actual indicator data to obtain a plurality of data indicators, and calculate an indicator score corresponding to each of the data indicators; An indicator weight calculation module, which is used to construct a hierarchical structure model using the hierarchical analysis method, and determine the weight value corresponding to each data indicator through the hierarchical structure model and industry expert knowledge; A data evaluation module, the data evaluation module is used to calculate the data quality score corresponding to the actual indicator data according to the indicator score and the weight value; The multiple data indicators include primary indicators and secondary indicators corresponding to the primary indicators; Among them, the first-level indicators include data integrity indicators, data consistency indicators, data accuracy indicators and data validity indicators; The secondary indicators corresponding to the data consistency index include speed consistency index, axle number consistency index, vehicle type consistency index and vehicle quantity consistency index; The secondary indicators corresponding to the data accuracy indicators include low-speed driving behavior indicators, speeding driving behavior indicators, parking driving behavior indicators, edge-pressing driving behavior indicators, lane-crossing driving behavior indicators, reverse driving behavior indicators, grinding driving behavior indicators, jumping driving behavior indicators, close following behavior indicators and environmental measurement status indicators; The secondary indicators corresponding to the data validity indicators include a multi-axle vehicle indicator, a broken axle vehicle indicator, a no upper scale signal indicator, and a no lower scale signal indicator; The indicator score corresponding to the data consistency indicator is calculated by the following formula: in, Indicates the indicator score corresponding to the data consistency indicator, Indicates the indicator score corresponding to the speed consistency indicator, Indicates the index score corresponding to the axis number consistency index, Indicates the index score corresponding to the vehicle model consistency index, Indicates the indicator score corresponding to the vehicle volume consistency indicator; in, Indicates the car speed value measured by the weighing equipment. Indicates the car speed value measured by the radar device. Represents the car speed value measured by the camera device. represents the average speed of the car, express , , The maximum value in express , , The minimum value in ; in, Indicates the index score corresponding to the axis number consistency index, Indicates the number of vehicle axles measured by the weighing equipment. Indicates the number of vehicle axles measured by the radar device, Indicates the number of vehicle axes measured by the camera device; in, Indicates the index score corresponding to the vehicle model consistency index, Indicates the car model data measured by the radar equipment, Represents the car model data measured by the camera device; in, Indicates the indicator score corresponding to the vehicle volume consistency indicator, Indicates the number of cars measured by the weighing equipment. Represents the number of cars measured by the camera device. Indicates the number of cars measured by the radar device.

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