Industrial vehicle carbon footprint data analysis method and system

Abstract

The embodiment of the invention provides an industrial vehicle carbon footprint data analysis method and system, and is applied to the field of industrial vehicle data analysis. And extracting industrial vehicle attribute data from the obtained industrial vehicle carbon footprint data, wherein the industrial vehicle attribute data comprises a time attribute grade, a technical attribute grade, a regional attribute grade and a precision attribute grade. Constructing a judgment matrix scale table of industrial vehicle attributes based on a quality matching similarity principle, and constructing a criterion layer judgment matrix of an analytic hierarchy process according to the judgment matrix scale table; and according to the judgment matrix scale table and the industrial vehicle attribute data, constructing a scheme layer judgment matrix of the analytic hierarchy process. Based on the criterion layer judgment matrix and the scheme layer judgment matrix, the data quality grade of the industrial vehicle carbon footprint data is obtained through analysis. The single-attribute influence and the attribute interaction influence of each industrial vehicle attribute on the industrial vehicle carbon footprint data quality are comprehensively considered, and the quality analysis accuracy of the industrial vehicle carbon footprint data is improved.

Description

Technical Field

[0001] The present application relates to the field of industrial vehicle data analysis, and specifically to an analysis method and system for industrial vehicle carbon footprint data. Background Art

[0002] The analysis of vehicle data plays an important role in the development of industries such as warehousing and logistics. Vehicle data related to carbon footprint can be called vehicle carbon footprint data. The quality evaluation of vehicle carbon footprint data is helpful for vehicle data analysis.

[0003] At present, the data quality of vehicle carbon footprint data is rated by using a user-defined data evaluation table, and the data quality is calculated by taking the average. It is believed that the multi-dimensional vehicle attributes have the same impact on data quality, that is, the weight of each vehicle attribute is consistent. However, in actual applications, the multi-dimensional vehicle attributes have different impacts on data quality. Therefore, the quality analysis of vehicle carbon footprint data has the problem of low accuracy. Summary of the invention

[0004] In view of this, the present application provides an analysis method and system for industrial vehicle carbon footprint data. The industrial vehicle carbon footprint data is analyzed through an optimized hierarchical analysis method, and a criterion layer judgment matrix and a scheme layer judgment matrix are constructed. The single attribute influence and attribute interaction influence of each industrial vehicle attribute on the quality of industrial vehicle carbon footprint data are comprehensively considered, thereby improving the accuracy of quality analysis of industrial vehicle carbon footprint data.

[0005] To solve the above problems, the technical solutions provided by this application are as follows: In a first aspect, an embodiment of the present application provides a method for analyzing carbon footprint data of industrial vehicles, the method comprising: In response to acquiring the industrial vehicle carbon footprint data, extracting industrial vehicle attribute data from the industrial vehicle carbon footprint data, wherein the industrial vehicle attribute data includes a time attribute level, a technical attribute level, a geographical attribute level, and an accuracy attribute level; Construct a judgment matrix scale table of industrial vehicle attributes based on the quality matching similarity principle; According to the judgment matrix scale table of the industrial vehicle attributes, a criterion layer judgment matrix of the hierarchical analysis method is constructed; the criterion layer judgment matrix is ​​used to indicate the importance of any two attributes of the time attribute, the technical attribute, the regional attribute and the accuracy attribute to the data quality of the industrial vehicle carbon footprint data; According to the judgment matrix scale table of the industrial vehicle attributes and the industrial vehicle attribute data, a scheme layer judgment matrix of the hierarchical analysis method is constructed; the scheme layer judgment matrix is ​​used to indicate the importance of each industrial vehicle attribute among the time attribute, the technical attribute, the regional attribute and the accuracy attribute to the data quality of the industrial vehicle carbon footprint data; Based on the criterion layer judgment matrix and the scheme layer judgment matrix of the hierarchical analysis method, the data quality level of the industrial vehicle carbon footprint data is analyzed and obtained.

[0006] In a possible implementation, the criterion layer judgment matrix and the scheme layer judgment matrix based on the hierarchical analysis method are analyzed to obtain the data quality level of the industrial vehicle carbon footprint data, including: Determine a first element in the eigenvector of the criterion layer judgment matrix and a second element corresponding to each industrial vehicle attribute in the eigenvector of the solution layer judgment matrix; Calculate the product of the first element and each of the second elements respectively, and the result of the product is the pending data quality result; The pending data quality results are sorted in descending order, wherein the pending data quality result ranked first is the target data quality result; The industrial vehicle attribute data corresponding to the target data quality result is used as the data quality level of the industrial vehicle carbon footprint data.

[0007] In a possible implementation, before the criterion layer judgment matrix and the scheme layer judgment matrix based on the hierarchical analysis method are analyzed to obtain the data quality level of the industrial vehicle carbon footprint data, the analysis method further includes: A consistency check is performed on the criterion layer judgment matrix and the solution layer judgment matrix to obtain a consistency check result.

[0008] In a possible implementation, the extracting industrial vehicle attribute data from the industrial vehicle carbon footprint data includes: Extracting the original time, original technology, original region and original precision from the industrial vehicle carbon footprint data; Based on a preset data quality grade table, the time attribute grade corresponding to the original time, the technical attribute grade corresponding to the original technology, the regional attribute grade corresponding to the original region, and the precision attribute grade corresponding to the original precision are determined.

[0009] In one possible implementation, the analysis method further includes: The industrial vehicle carbon footprint data is managed based on the data quality level.

[0010] In a second aspect, an embodiment of the present application provides an analysis system for industrial vehicle carbon footprint data, the analysis system comprising: an extraction module, configured to extract industrial vehicle attribute data from the industrial vehicle carbon footprint data in response to acquiring the industrial vehicle carbon footprint data, wherein the industrial vehicle attribute data includes a time attribute level, a technical attribute level, a regional attribute level, and an accuracy attribute level; A construction module for constructing a judgment matrix scaling table of industrial vehicle attributes based on the quality matching similarity principle; The construction module is further used to construct a criterion layer judgment matrix of the hierarchical analysis method according to the judgment matrix scale table of the industrial vehicle attributes; the criterion layer judgment matrix is ​​used to indicate the importance of any two attributes of the time attribute, the technical attribute, the regional attribute and the accuracy attribute to the data quality of the industrial vehicle carbon footprint data; The construction module is further used to construct a scheme-level judgment matrix of the hierarchical analysis method according to the judgment matrix scale table of the industrial vehicle attributes and the industrial vehicle attribute data; the scheme-level judgment matrix is ​​used to indicate the importance of each industrial vehicle attribute among the time attribute, the technical attribute, the regional attribute and the accuracy attribute to the data quality of the industrial vehicle carbon footprint data; The analysis module is used to analyze and obtain the data quality level of the industrial vehicle carbon footprint data based on the criterion layer judgment matrix and the scheme layer judgment matrix of the hierarchical analysis method.

[0011] In a possible implementation, the analysis module is specifically used to: Determine a first element in the eigenvector of the criterion layer judgment matrix and a second element corresponding to each industrial vehicle attribute in the eigenvector of the solution layer judgment matrix; Calculate the product of the first element and each of the second elements respectively, and the result of the product is the pending data quality result; The pending data quality results are sorted in descending order of numerical values, wherein the pending data quality result ranked first is the target data quality result; The industrial vehicle attribute data corresponding to the target data quality result is used as the data quality level of the industrial vehicle carbon footprint data.

[0012] In a possible implementation, the analysis system further includes a detection module: The detection module is used to perform consistency detection on the criterion layer judgment matrix and the scheme layer judgment matrix of the hierarchical analysis method before calculating the data quality level of the industrial vehicle carbon footprint data to obtain a consistency detection result.

[0013] In a possible implementation, the extraction module is used to extract industrial vehicle attribute data from the industrial vehicle carbon footprint data, including: An extraction submodule, used to extract the original time, original technology, original region and original precision from the industrial vehicle carbon footprint data; A determination submodule is used to determine the time attribute level corresponding to the original time, the technical attribute level corresponding to the original technology, the regional attribute level corresponding to the original region, and the accuracy attribute level corresponding to the original accuracy based on a preset data quality level table.

[0014] In a possible implementation, the analysis system further includes: A management module is used to manage the industrial vehicle carbon footprint data based on the data quality level.

[0015] In a third aspect, an embodiment of the present application provides an analysis device for industrial vehicle carbon footprint data, the device comprising: a processor, a memory, and a system bus; The processor and the memory are connected via the system bus; The memory is used to store one or more programs, and the one or more programs include instructions. When the instructions are executed by the processor, the processor executes the method for analyzing carbon footprint data of industrial vehicles described in the first aspect.

[0016] In a fourth aspect, an embodiment of the present application provides a storage medium, wherein the storage medium stores instructions, and when the instructions are executed by a computer, the computer implements the method for analyzing carbon footprint data of industrial vehicles described in the first aspect.

[0017] It can be seen that this application has the following beneficial effects: The embodiment of the present application provides an analysis method for industrial vehicle carbon footprint data. First, industrial vehicle attribute data is extracted from the acquired industrial vehicle carbon footprint data. The industrial vehicle attribute data includes time attribute level, technical attribute level, regional attribute level and precision attribute level. Secondly, a judgment matrix scale table of industrial vehicle attributes is constructed based on the quality matching similarity principle. According to the judgment matrix scale table, a criterion layer judgment matrix of the hierarchical analysis method is constructed; according to the judgment matrix scale table and the industrial vehicle attribute data, a scheme layer judgment matrix of the hierarchical analysis method is constructed. Among them, the criterion layer judgment matrix is ​​used to indicate the importance of any two attributes among multiple industrial vehicle attributes to the data quality of industrial vehicle carbon footprint data; the scheme layer judgment matrix is ​​used to indicate the importance of each industrial vehicle attribute among multiple industrial vehicle attributes to the data quality of industrial vehicle carbon footprint data. Finally, based on the criterion layer judgment matrix and scheme layer judgment matrix of the hierarchical analysis method, the data quality level of the industrial vehicle carbon footprint data is analyzed. In this way, the carbon footprint data of industrial vehicles were analyzed through the optimized hierarchical analysis method, and the criterion layer judgment matrix and the scheme layer judgment matrix were constructed. The single attribute influence and attribute interaction influence of each industrial vehicle attribute on the quality of industrial vehicle carbon footprint data were comprehensively considered, thereby improving the accuracy of quality analysis of industrial vehicle carbon footprint data.

[0018] The embodiment of the present application also provides a system corresponding to the above method, which has the same beneficial effects as the above method. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are some implementation methods of the present invention, and for ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0020] Figure 1 A schematic diagram of a conventional hierarchical analysis process provided in an embodiment of the present application; Figure 2 A schematic flow chart of an analysis method for industrial vehicle carbon footprint data provided in an embodiment of the present application; Figure 3 A schematic diagram of a data quality evaluation hierarchy structure provided in an embodiment of the present application; Figure 4 A schematic diagram of an optimized hierarchical analysis method flow chart provided in an embodiment of the present application; Figure 5 A schematic diagram of the carbon footprint life cycle of an industrial vehicle provided in an embodiment of the present application; Figure 6A schematic diagram of the structure of an industrial vehicle carbon footprint data analysis system provided in an embodiment of the present application; Figure 7 A schematic diagram of the structure of an industrial vehicle carbon footprint data analysis device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0022] In this application, the terms "comprises", "comprising" or any other variation thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element.

[0023] The existing technology uses a user-set data evaluation table to rate the data quality of industrial vehicle carbon footprint data, and calculates the data quality by taking the average. It is believed that the multi-dimensional industrial vehicle attributes have the same impact on data quality, that is, the weight of each industrial vehicle attribute is consistent. However, in actual applications, the multi-dimensional industrial vehicle attributes have different impacts on data quality. Therefore, the quality analysis of industrial vehicle carbon footprint data has the problem of low accuracy.

[0024] The data evaluation table set by the user may be referred to as the data quality rating (DQR) of the initial data, see Table 1 below.

[0025]

[0026] The common carbon footprint evaluation standard is to calculate data quality using the average method. See the following formula (1) to calculate the data quality rating of the data set, taking the average of time, technology, region and accuracy.

[0027] Formula (1) Among them, DQR is the data quality rating of the dataset, Te is the technical representativeness, Ge is the geographical representativeness, Ti is the time-related representativeness, and P is the parameter precision.

[0028] This method assumes that the four dimensions have the same impact on data quality. However, in actual applications, the four dimensions of time, technology, region and accuracy have different impacts on data quality. This method does not take all factors into consideration, and the final results cannot truly reflect the actual data quality.

[0029] The embodiment of the present application is aimed at evaluating the quality of collected data during the carbon footprint accounting and verification process of industrial vehicles. Taking into account the impact of the importance of the four dimensions on the data quality of industrial vehicle carbon footprint data and the comparison between the data quality of the four dimensions, the theory of hierarchical analysis is introduced, and the quality evaluation process is considered more comprehensive and objective through mathematical methods. It should be noted that the embodiment of the present application is not limited to the quality evaluation of industrial vehicle carbon footprint data, but can also be applied to other similar problems.

[0030] The core of the hierarchical analysis method is to introduce measurement theory on the basis of establishing a clear hierarchical structure to decompose complex problems. By comparing two by two, the user's judgment is scalared with a relative scale, and the corresponding judgment matrix is ​​established layer by layer. The eigenvector of the judgment matrix is ​​then solved to obtain the comprehensive weight of each element. Finally, the rationality of the weight factor is verified by the consistency ratio.

[0031] See also Figure 1 , Figure 1 A conventional AHP flow chart is provided for the embodiment of the present application. In the process of constructing the judgment matrix, the conventional AHP cannot construct the judgment matrix of the scheme layer. In order to ensure the objectivity of data quality, the AHP theory is introduced, and the AHP is improved to make it suitable for high-precision data quality evaluation.

[0032] Specifically, an embodiment of the present application provides an analysis method and system for industrial vehicle carbon footprint data. First, industrial vehicle attribute data is extracted from the acquired industrial vehicle carbon footprint data, and the industrial vehicle attribute data includes time attribute level, technical attribute level, regional attribute level and precision attribute level. Secondly, a judgment matrix scale table of industrial vehicle attributes is constructed based on the quality matching similarity principle, and a criterion layer judgment matrix of the hierarchical analysis method is constructed according to the judgment matrix scale table; and a scheme layer judgment matrix of the hierarchical analysis method is constructed according to the judgment matrix scale table and the industrial vehicle attribute data. Among them, the criterion layer judgment matrix is ​​used to indicate the importance of any two attributes among multiple industrial vehicle attributes to the data quality of the industrial vehicle carbon footprint data; the scheme layer judgment matrix is ​​used to indicate the importance of each industrial vehicle attribute among multiple industrial vehicle attributes to the data quality of the industrial vehicle carbon footprint data. Finally, based on the criterion layer judgment matrix and scheme layer judgment matrix of the hierarchical analysis method, the data quality level of the industrial vehicle carbon footprint data is analyzed. In this way, the carbon footprint data of industrial vehicles were analyzed through the optimized hierarchical analysis method, and the criterion layer judgment matrix and the scheme layer judgment matrix were constructed. The single attribute influence and attribute interaction influence of each industrial vehicle attribute on the quality of industrial vehicle carbon footprint data were comprehensively considered, thereby improving the accuracy of quality analysis of industrial vehicle carbon footprint data.

[0033] In order to facilitate understanding of the technical solution provided in the embodiment of the present application, an industrial vehicle carbon footprint data analysis method and system provided in the embodiment of the present application are described below with reference to the accompanying drawings.

[0034] See also Figure 2 , Figure 2 A schematic flow chart of an industrial vehicle carbon footprint data analysis method provided in an embodiment of the present application. The industrial vehicle carbon footprint data analysis method can be applied to a server or system, which is not limited in the present embodiment and can be selected according to actual needs. The evaluation method specifically includes S201-S205.

[0035] S201: In response to acquiring industrial vehicle carbon footprint data, extracting industrial vehicle attribute data from the industrial vehicle carbon footprint data, wherein the industrial vehicle attribute data includes a time attribute level, a technical attribute level, a regional attribute level, and an accuracy attribute level.

[0036] The data used in the carbon footprint data accounting of industrial vehicles is affected by time, technology, region, accuracy, etc. Therefore, ensuring that the data quality evaluation is reasonable is the key to ensuring the credibility of the carbon footprint calculation and is helpful for the comparison of the carbon footprint of industrial vehicles. Among them, this method can be applied to industrial vehicles or other types of vehicles, and the embodiments of this application are not limited to this.

[0037] In order to smoothly introduce the hierarchical analysis method, a judgment basis for the data quality level is provided in advance for different problem requirements, as shown in Table 2. Different from the quantitative judgment used in the prior art, the embodiment of the present application does not involve specific scores, but uses qualitative judgment to determine the most matching data quality.

[0038]

[0039] In one possible implementation, the extracting of industrial vehicle attribute data from the industrial vehicle carbon footprint data includes: extracting the original time, original technology, original region, and original precision in the industrial vehicle carbon footprint data; and determining, based on a preset data quality grade table, a time attribute grade corresponding to the original time, a technical attribute grade corresponding to the original technology, a regional attribute grade corresponding to the original region, and a precision attribute grade corresponding to the original precision.

[0040] Industrial vehicle carbon footprint data includes data such as original time, original technology, original region, and original accuracy. After extracting the original data of the above four dimensions from the industrial vehicle carbon footprint data, the grade data corresponding to the original time, original technology, original region, and original accuracy are searched one by one in the preset data quality grade table to obtain the industrial vehicle attribute data. The industrial vehicle attribute data may include the time attribute grade, technology attribute grade, region attribute grade, and accuracy attribute grade.

[0041] S202: Constructing a judgment matrix scaling table of industrial vehicle attributes based on the quality matching similarity principle.

[0042] The matching similarity principle of quality is used to measure the scale of the constructed judgment matrix, that is, to construct a comparison matrix of data quality in four dimensions. The scale table of the judgment matrix of industrial vehicle attributes is shown in Table 3 below.

[0043]

[0044] The mathematical explanation of the quality matching similarity principle is: specify a set of quality levels , the quality matching degree corresponding to this quality level is If the data The best match, then the matching relationship of the quality level is: , and if there are and , ,So .

[0045] Through the judgment matrix scale table of industrial vehicle attributes, the data level division of the judgment matrix can be determined. Figure 3 , Figure 3A schematic diagram of a data quality evaluation hierarchy provided in an embodiment of the present application. The importance of the four dimensions is considered when constructing the criterion layer judgment matrix, the construction scheme layer judgment matrix is ​​improved, and the data quality of the four dimensions is compared, as shown below.

[0046] S203: constructing a criterion layer judgment matrix of the hierarchical analysis method according to the judgment matrix scale table of the industrial vehicle attributes; the criterion layer judgment matrix is ​​used to indicate the importance of any two attributes among the time attribute, technical attribute, regional attribute and accuracy attribute to the data quality of the industrial vehicle carbon footprint data.

[0047] Criteria layer judgment matrix

[0048] in, express right The relative importance of , in the criterion layer judgment matrix U, n=4, where n is the number of criterion layers.

[0049] A criterion-level judgment matrix is ​​constructed between data quality and time, technology, region, and accuracy to compare the importance of any two attributes among time, technology, region, and accuracy to data quality.

[0050] S204: Constructing a scheme-level judgment matrix of the analytic hierarchy process according to the judgment matrix scale table of the industrial vehicle attributes and the industrial vehicle attribute data; the scheme-level judgment matrix is ​​used to indicate the importance of each industrial vehicle attribute among the time attribute, the technical attribute, the regional attribute and the accuracy attribute to the data quality of the industrial vehicle carbon footprint data.

[0051] Time and solution level judgment matrix ; Technology and solution level judgment matrix ; Judgment matrix for region and solution level ; Precision and solution level judgment matrix .

[0052] in, express right The relative importance of , in the time and solution level judgment matrix W In, n=4. Same reason Determine the judgment matrix of time and excellent, good, average, and poor, the judgment matrix of technology and excellent, good, average, and poor, the judgment matrix of region and excellent, good, average, and poor, and the judgment matrix of accuracy and excellent, good, average, and poor. Further judge whether the carbon footprint data of industrial vehicles is closer to excellent, good, average, or poor in terms of time, technology, region, and accuracy.

[0053] S205: Analyze and obtain the data quality level of the industrial vehicle carbon footprint data based on the criterion layer judgment matrix and the solution layer judgment matrix of the hierarchical analysis method.

[0054] Before analyzing the data quality level of industrial vehicle carbon footprint data based on the criterion layer judgment matrix and the scheme layer judgment matrix of the hierarchical analysis method, it is necessary to perform consistency detection on the criterion layer judgment matrix and the scheme layer judgment matrix to obtain the consistency detection result.

[0055] The judgment matrix obtained by pairwise comparison does not necessarily satisfy consistency. In order to ensure the accuracy and credibility of the judgment matrix and avoid the influence of user subjective factors on the judgment matrix, the embodiment of the present application constructs the judgment matrix. Perform consistency check. See formula (2) for the verification formula: Formula (2) in, CR is the random consistency ratio, CI is the consistency indicator, RI is the average random consistency index.

[0056] when CR< When , the inconsistency is within an acceptable range, that is, it also meets the requirements, and the judgment matrix passes the consistency test. In the embodiment of the present application, the preset threshold value can be 0.1, and the value of the preset threshold value is not limited, and can be selected according to actual needs.

[0057] Formula (3) is the maximum eigenvalue of the judgment matrix, and n is the matrix order of the judgment matrix. The matrix order of the judgment matrix is ​​equal to the number of criterion layers.

[0058] RI The relationship between the value of and the influencing factor n can be found by looking up RI-n The relationship table is obtained, see Table 4 below for details.

[0059]

[0060] After consistency detection, the data quality level of industrial vehicle carbon footprint data is analyzed based on the criterion layer judgment matrix and scheme layer judgment matrix of the hierarchical analysis method.

[0061] Determine the first element in the eigenvector of the criterion layer judgment matrix and the second element corresponding to each industrial vehicle attribute in the eigenvector of the scheme layer judgment matrix; calculate the product of the first element and each second element respectively, and the result of the product is the pending data quality result; sort the pending data quality results in order from large to small, wherein the pending data quality result ranked first is the target data quality result; and use the industrial vehicle attribute data corresponding to the target data quality result as the data quality level of the industrial vehicle carbon footprint data.

[0062] Specifically, the final calculation is performed using formula (4).

[0063] Formula (4) in, is the criterion layer judgment matrix U The eigenvector of The first element in ; is the solution level judgment matrix The eigenvector of , , , The second elements of , , , The eigenvectors of each judgment matrix can be calculated using standard mathematical operations.

[0064] First Element With the second element The product of Pending data quality results. Pending data quality results may include ,from Select the pending data quality result with the largest value as the target data quality result P .

[0065] calculate Respectively The product of , we get the undetermined data quality result. and The corresponding pending data quality result has the largest value, and the time attribute level corresponding to the time attribute is the data quality level of the industrial vehicle carbon footprint data.

[0066] See also Figure 4 , Figure 4This is a schematic diagram of an optimized hierarchical analysis method flow chart provided in the embodiment of the present application. The importance of multiple dimensions is considered when constructing the criterion layer judgment matrix, and the construction scheme layer judgment matrix is ​​improved. Figure 4 The process steps are shown in the dashed box.

[0067] In one possible implementation, industrial vehicle carbon footprint data may be managed based on data quality levels.

[0068] For industrial vehicle carbon footprint data with poor or average data quality, users can optimize industrial production processes by analyzing the industrial vehicle carbon footprint data. For industrial vehicle carbon footprint data with excellent or good data quality, users can use it to assist in optimizing other industrial production processes.

[0069] The carbon footprint data of industrial vehicles can also be updated and maintained regularly. The timely update of the carbon footprint data of industrial vehicles can improve the accuracy of data analysis and ensure the accuracy of carbon footprint calculation.

[0070] Based on the contents of the above steps S201-S205, it can be known that industrial vehicle attribute data is extracted from the acquired industrial vehicle carbon footprint data, and the industrial vehicle attribute data includes time attribute level, technical attribute level, regional attribute level and precision attribute level. A judgment matrix scale table of industrial vehicle attributes is constructed based on the principle of quality matching similarity, and a criterion layer judgment matrix of the hierarchical analysis method is constructed according to the judgment matrix scale table; a scheme layer judgment matrix of the hierarchical analysis method is constructed according to the judgment matrix scale table and the industrial vehicle attribute data. Among them, the criterion layer judgment matrix is ​​used to indicate the importance of any two attributes of multiple industrial vehicle attributes to the data quality of industrial vehicle carbon footprint data; the scheme layer judgment matrix is ​​used to indicate the importance of each industrial vehicle attribute of multiple industrial vehicle attributes to the data quality of industrial vehicle carbon footprint data. Finally, based on the criterion layer judgment matrix and scheme layer judgment matrix of the hierarchical analysis method, the data quality level of the industrial vehicle carbon footprint data is analyzed. In this way, the carbon footprint data of industrial vehicles were analyzed through the optimized hierarchical analysis method, and the criterion layer judgment matrix and the scheme layer judgment matrix were constructed. The single attribute influence and attribute interaction influence of each industrial vehicle attribute on the quality of industrial vehicle carbon footprint data were comprehensively considered, thereby improving the accuracy of quality analysis of industrial vehicle carbon footprint data.

[0071] Another embodiment of the present application takes forklift production as an example to illustrate the technical solution of the present application.

[0072] See also Figure 5 , Figure 5A schematic diagram of the carbon footprint life cycle of an industrial vehicle provided in an embodiment of the present application is provided. Energy data is evaluated, for example, for the product manufacturing stage in the life cycle of an industrial vehicle, general data can be found in Table 5.

[0073]

[0074] In the embodiments of the present application, the evaluation of the data source is an important criterion for judging the data quality, and the specific value is not used for data quality evaluation. For example, the power consumed by the assembly of the production line can be calculated by using the power and time of the assembly equipment; it can also be obtained directly from the electric meter.

[0075] The method of obtaining electricity through an electric meter is more accurate, but not every device in every production line is equipped with an electric meter. In actual applications, there are also a variety of data acquisition methods, and the reliability of the data source is the key to evaluating data quality.

[0076] Based on the data source, the specific content of the aforementioned Table 2 can be constructed. Considering the accuracy attribute, the data quality of the power data obtained by the method of obtaining power using an electric meter is excellent; the data quality of the power data obtained by the power and time calculation method of the assembled equipment is good.

[0077] Suppose that a quality evaluation is conducted on the energy consumption data of a certain model of product production line in a forklift production workshop, and the evaluation levels are divided into excellent, good, average, and poor.

[0078] The criterion layers are compared pairwise to compare the importance of time, technology, region and accuracy for industrial vehicle carbon footprint data. The comparison results in Table 6.

[0079]

[0080] The criterion layer judgment matrix is:

[0081] The scheme layers are compared pairwise, that is, the data quality is evaluated under the premise of considering time, technology, region and accuracy separately, and the following matrix is ​​obtained by combining the quality matching similarity principle.

[0082] From the perspective of time, the data is excellent, and the judgment matrix is:

[0083] From a technical perspective, the data is good and the judgment matrix is:

[0084] From a regional perspective, the data is general and the judgment matrix is:

[0085] From the perspective of accuracy, the data is poor, and the judgment matrix is:

[0086] Judgment Matrix and A consistency test was performed, and the CR values ​​were 0.035, 0.035, 0.012, 0.012, and 0.035, respectively. The CR values ​​of the judgment matrix were all less than 0.1, and the consistency test passed.

[0087] The quality result of the undetermined data is calculated as follows: , the value of the pending data quality result corresponding to the precision attribute is the largest, and it is believed that the precision attribute is more important for the industrial vehicle carbon footprint data. Therefore, the data quality level of the precision attribute is used as the data quality level of the industrial vehicle carbon footprint data. Since the precision attribute level corresponding to the precision attribute is poor, the final data quality level of the industrial vehicle carbon footprint data is also poor.

[0088] Based on the above calculation results, in the process of data management, the pending data quality results can be sorted from large to small according to their values, focusing on optimizing the attributes of industrial vehicles ranked first.

[0089] Based on the above-mentioned embodiments, the data is divided into multiple levels in a qualitative manner, and the construction of the scheme-level judgment matrix is ​​realized based on the principle of quality matching similarity. The judgment matrix of data quality is obtained by pairwise comparison, and mathematical methods are used for consistency verification to avoid the problem of low evaluation accuracy caused by subjective reasons or errors. The importance of the four dimensions is considered when constructing the criterion-level judgment matrix, and the quality comparison between the data of the four dimensions is considered when constructing the scheme judgment matrix. The importance of each industrial vehicle attribute to the vehicle-connected carbon footprint data analysis can be determined, and the impact of multiple factors on data analysis is fully considered. Based on this, the data analysis is completed, and the accuracy of the industrial vehicle carbon footprint data analysis is improved.

[0090] The above-mentioned embodiment of the present application provides an analysis method for industrial vehicle carbon footprint data based on the above-mentioned. Next, an analysis system for industrial vehicle carbon footprint data also provided in the embodiment of the present application is described, which is used to perform the above-mentioned Figure 2 The function of the industrial vehicle carbon footprint data analysis system is described. The structural diagram of the industrial vehicle carbon footprint data analysis system is shown in FIG. Figure 6 As shown, it includes an extraction module 601, a construction module 602 and an analysis module 603.

[0091] in, An extraction module 601 is used for extracting industrial vehicle attribute data from the industrial vehicle carbon footprint data in response to obtaining the industrial vehicle carbon footprint data, wherein the industrial vehicle attribute data includes a time attribute level, a technical attribute level, a regional attribute level, and an accuracy attribute level; A construction module 602 is used to construct a judgment matrix scale table of industrial vehicle attributes based on the quality matching similarity principle; The construction module 602 is further used to construct a criterion layer judgment matrix of the hierarchical analysis method according to the judgment matrix scale table of the industrial vehicle attributes; the criterion layer judgment matrix is ​​used to indicate the importance of any two attributes of the time attribute, the technical attribute, the regional attribute and the accuracy attribute to the data quality of the industrial vehicle carbon footprint data; The construction module 602 is further used to construct a scheme-level judgment matrix of the hierarchical analysis method according to the judgment matrix scale table of the industrial vehicle attributes and the industrial vehicle attribute data; the scheme-level judgment matrix is ​​used to indicate the importance of each industrial vehicle attribute among the time attribute, the technical attribute, the regional attribute and the accuracy attribute to the data quality of the industrial vehicle carbon footprint data; The analysis module 603 is used to analyze and obtain the data quality level of the industrial vehicle carbon footprint data based on the criterion layer judgment matrix and the scheme layer judgment matrix of the hierarchical analysis method.

[0092] In a possible implementation, the analysis module 603 is specifically used to: Determine a first element in the eigenvector of the criterion layer judgment matrix and a second element corresponding to each industrial vehicle attribute in the eigenvector of the solution layer judgment matrix; Calculate the product of the first element and each of the second elements respectively, and the result of the product is the pending data quality result; The pending data quality results are sorted in descending order of numerical values, wherein the pending data quality result ranked first is the target data quality result; The industrial vehicle attribute data corresponding to the target data quality result is used as the data quality level of the industrial vehicle carbon footprint data.

[0093] In a possible implementation, the analysis system further includes a detection module: The detection module is used to perform consistency detection on the criterion layer judgment matrix and the scheme layer judgment matrix of the hierarchical analysis method before calculating the data quality level of the industrial vehicle carbon footprint data to obtain a consistency detection result.

[0094] In a possible implementation, the extraction module 601 is used to extract industrial vehicle attribute data from the industrial vehicle carbon footprint data, including: An extraction submodule, used to extract the original time, original technology, original region and original precision from the industrial vehicle carbon footprint data; A determination submodule is used to determine the time attribute level corresponding to the original time, the technical attribute level corresponding to the original technology, the regional attribute level corresponding to the original region, and the accuracy attribute level corresponding to the original accuracy based on a preset data quality level table.

[0095] In a possible implementation, the analysis system further includes: A management module is used to manage the industrial vehicle carbon footprint data based on the data quality level.

[0096] The embodiment of the present application provides an analysis system for industrial vehicle carbon footprint data, including an extraction module, a construction module and an analysis module. The extraction module is used to extract industrial vehicle attribute data from the industrial vehicle carbon footprint data in response to obtaining the industrial vehicle carbon footprint data, wherein the industrial vehicle attribute data includes a time attribute level, a technical attribute level, a regional attribute level and a precision attribute level. The construction module is used to construct a judgment matrix scale table of industrial vehicle attributes based on the quality matching similarity principle; according to the judgment matrix scale table of industrial vehicle attributes, a criterion layer judgment matrix of the hierarchical analysis method is constructed; the criterion layer judgment matrix is ​​used to indicate the importance of any two attributes of the time attribute, the technical attribute, the regional attribute and the precision attribute to the data quality of the industrial vehicle carbon footprint data; according to the judgment matrix scale table of industrial vehicle attributes and the industrial vehicle attribute data, a scheme layer judgment matrix of the hierarchical analysis method is constructed; the scheme layer judgment matrix is ​​used to indicate the importance of each industrial vehicle attribute of the time attribute, the technical attribute, the regional attribute and the precision attribute to the data quality of the industrial vehicle carbon footprint data. The analysis module is used to analyze and obtain the data quality level of the industrial vehicle carbon footprint data based on the criterion layer judgment matrix and the scheme layer judgment matrix of the hierarchical analysis method. In this way, the industrial vehicle carbon footprint data is analyzed by the optimized hierarchical analysis method, and the criterion layer judgment matrix and the scheme layer judgment matrix are constructed, which comprehensively considers the single attribute influence and attribute interaction influence of each industrial vehicle attribute on the quality of the industrial vehicle carbon footprint data, thereby improving the accuracy of the quality analysis of the industrial vehicle carbon footprint data.

[0097] Based on the above method embodiment, a method for analyzing carbon footprint data of industrial vehicles is provided. The present application embodiment also provides an analysis device for carbon footprint data of industrial vehicles. Figure 7, the device includes: a processor, a memory, and a system bus; The processor and the memory are connected via the system bus; The memory is used to store one or more programs, and the one or more programs include instructions. When the instructions are executed by the processor, the processor executes the method for analyzing carbon footprint data of industrial vehicles described in any one of the above embodiments.

[0098] Based on the method for analyzing the carbon footprint data of industrial vehicles provided in the above method embodiment, the embodiment of the present application also provides a storage medium, which stores instructions. When the instructions are executed by a computer, the computer implements the method for analyzing the carbon footprint data of industrial vehicles described in the first aspect above.

[0099] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without creative work.

[0100] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0101] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for analyzing carbon footprint data of industrial vehicles, characterized in that: The analysis method comprises: In response to acquiring the industrial vehicle carbon footprint data, extracting industrial vehicle attribute data from the industrial vehicle carbon footprint data, wherein the industrial vehicle attribute data includes a time attribute level, a technical attribute level, a geographical attribute level, and an accuracy attribute level; Construct a judgment matrix scale table of industrial vehicle attributes based on the quality matching similarity principle; According to the judgment matrix scale table of the industrial vehicle attributes, a criterion layer judgment matrix of the hierarchical analysis method is constructed; the criterion layer judgment matrix is ​​used to indicate the importance of any two attributes of the time attribute, the technical attribute, the regional attribute and the accuracy attribute to the data quality of the industrial vehicle carbon footprint data; According to the judgment matrix scale table of the industrial vehicle attributes and the industrial vehicle attribute data, a scheme layer judgment matrix of the hierarchical analysis method is constructed; the scheme layer judgment matrix is ​​used to indicate the importance of each industrial vehicle attribute among the time attribute, the technical attribute, the regional attribute and the accuracy attribute to the data quality of the industrial vehicle carbon footprint data; Based on the criterion layer judgment matrix and the scheme layer judgment matrix of the hierarchical analysis method, the data quality level of the industrial vehicle carbon footprint data is analyzed and obtained.

2. The analysis method according to claim 1, characterized in that The criterion layer judgment matrix and the scheme layer judgment matrix based on the hierarchical analysis method are used to analyze and obtain the data quality level of the industrial vehicle carbon footprint data, including: Determine a first element in the eigenvector of the criterion layer judgment matrix and a second element corresponding to each industrial vehicle attribute in the eigenvector of the solution layer judgment matrix; Calculate the product of the first element and each of the second elements respectively, and the result of the product is the pending data quality result; The pending data quality results are sorted in descending order, wherein the pending data quality result ranked first is the target data quality result; The industrial vehicle attribute data corresponding to the target data quality result is used as the data quality level of the industrial vehicle carbon footprint data.

3. The analysis method according to any one of claims 1 to 2, characterized in that Before the criterion layer judgment matrix and the scheme layer judgment matrix based on the hierarchical analysis method are analyzed to obtain the data quality level of the industrial vehicle carbon footprint data, the analysis method further includes: A consistency check is performed on the criterion layer judgment matrix and the solution layer judgment matrix to obtain a consistency check result.

4. The analysis method according to claim 1, characterized in that The step of extracting industrial vehicle attribute data from the industrial vehicle carbon footprint data comprises: Extracting the original time, original technology, original region and original precision from the industrial vehicle carbon footprint data; Based on a preset data quality grade table, the time attribute grade corresponding to the original time, the technical attribute grade corresponding to the original technology, the regional attribute grade corresponding to the original region, and the precision attribute grade corresponding to the original precision are determined.

5. The analysis method according to claim 1, characterized in that The analysis method further comprises: The industrial vehicle carbon footprint data is managed based on the data quality level.

6. An analysis system for industrial vehicle carbon footprint data, characterized in that: The analysis system comprises: an extraction module, configured to extract industrial vehicle attribute data from the industrial vehicle carbon footprint data in response to acquiring the industrial vehicle carbon footprint data, wherein the industrial vehicle attribute data includes a time attribute level, a technical attribute level, a regional attribute level, and an accuracy attribute level; A construction module for constructing a judgment matrix scaling table of industrial vehicle attributes based on the quality matching similarity principle; The construction module is further used to construct a criterion layer judgment matrix of the hierarchical analysis method according to the judgment matrix scale table of the industrial vehicle attributes; the criterion layer judgment matrix is ​​used to indicate the importance of any two attributes of the time attribute, the technical attribute, the regional attribute and the accuracy attribute to the data quality of the industrial vehicle carbon footprint data; The construction module is further used to construct a scheme-level judgment matrix of the hierarchical analysis method according to the judgment matrix scale table of the industrial vehicle attributes and the industrial vehicle attribute data; the scheme-level judgment matrix is ​​used to indicate the importance of each industrial vehicle attribute among the time attribute, the technical attribute, the regional attribute and the accuracy attribute to the data quality of the industrial vehicle carbon footprint data; The analysis module is used to analyze and obtain the data quality level of the industrial vehicle carbon footprint data based on the criterion layer judgment matrix and the scheme layer judgment matrix of the hierarchical analysis method.

7. The analysis system according to claim 6, characterized in that The analysis module is specifically used for: Determine a first element in the eigenvector of the criterion layer judgment matrix and a second element corresponding to each industrial vehicle attribute in the eigenvector of the solution layer judgment matrix; Calculate the product of the first element and each of the second elements respectively, and the result of the product is the pending data quality result; The pending data quality results are sorted in descending order of numerical values, wherein the pending data quality result ranked first is the target data quality result; The industrial vehicle attribute data corresponding to the target data quality result is used as the data quality level of the industrial vehicle carbon footprint data.

8. The analysis system according to any one of claims 6 to 7, characterized in that: The analysis system also includes a detection module: The detection module is used to perform consistency detection on the criterion layer judgment matrix and the scheme layer judgment matrix of the hierarchical analysis method before calculating the data quality level of the industrial vehicle carbon footprint data to obtain a consistency detection result.

9. The analysis system according to claim 6, characterized in that The extraction module is used to extract industrial vehicle attribute data from the industrial vehicle carbon footprint data, including: An extraction submodule, used to extract the original time, original technology, original region and original precision from the industrial vehicle carbon footprint data; A determination submodule is used to determine the time attribute level corresponding to the original time, the technical attribute level corresponding to the original technology, the regional attribute level corresponding to the original region, and the accuracy attribute level corresponding to the original accuracy based on a preset data quality level table.

10. The analysis system according to claim 6, characterized in that The analysis system further comprises: A management module is used to manage the industrial vehicle carbon footprint data based on the data quality level.

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