CN110991974A - GPS-based transportation cost intelligent accounting system and method - Google Patents
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Abstract
The invention relates to the field of logistics cost accounting, in particular to a GPS-based transportation cost intelligent accounting system and a GPS-based transportation cost intelligent accounting method, wherein the GPS-based transportation cost intelligent accounting system comprises a GPS module, a server, a remote control module and a display screen, wherein the GPS module is arranged on a transportation tool or important goods and is connected with the server through a wireless network; the server is connected with the remote control module through a wireless network; the server accounting step includes, S1: extracting expense data, S2: classifying the expense data based on the distance index by adopting a clustering analysis algorithm, S3: and (3) establishing a mathematical model by using the classified expense data as independent variables and the transportation cost as dependent variables through multivariate regression analysis, and determining influence factors of various expenses, S4: and outputting the data to a display screen. The method can determine the influence factors of various expenses so as to take measures in a targeted manner to reduce the transportation cost; the position generated by various expenses can be monitored in real time through GPS real-time positioning.
Description
Technical Field
The invention relates to the field of logistics cost accounting, in particular to a GPS-based transportation cost intelligent accounting system and method.
Background
In recent years, the real estate market has developed in large scale, and new rural construction, such as fire redundancy, has progressed, which means that the demand for building materials has increased. At present, manual arrangement, manual bills and manual loading and unloading operations are adopted in the transportation process of most building materials on a construction site, an information system is difficult to track products in time and obtain in-transit information, the requirements of customers cannot be effectively met, and meanwhile transportation cost management faces new challenges. In view of the above, the document CN109993483A discloses a logistics distribution system for product production links and a logistics cost accounting method thereof, which includes a distribution center, a logistics center, a GPS positioning terminal located on a transportation vehicle, RFID electronic tags attached to products, and mobile intelligent terminals used by different persons; the mobile intelligent terminal comprises a sender mobile intelligent terminal, a receiver mobile intelligent terminal and a distributor mobile intelligent terminal; the distribution center comprises a distribution server and an online picking subsystem; the logistics center comprises a logistics server and a warehouse-out RFID card reader. The system establishes a service system consisting of multi-level logistics consisting of a production line, a distribution center and a logistics center, and utilizes the acquisition equipment to acquire the cost of each department in the production and transportation process of the product and the vehicle cost information in the transportation process in real time, so that the logistics cost accounting progress is improved, and the management intensity of the product is improved.
Because the expenses generated in the transportation process are scattered and various, some expenses have a part of set intersection in the classification. Therefore, the existing transportation cost accounting system is difficult to accurately classify expenses generated in the transportation process of the building materials according to the essential characteristics of the expenses; and a mathematical model cannot be established by taking factors influencing the transportation cost as independent variables and the transportation cost as dependent variables, and influence factors of various expenses are determined, so that measures are taken in a targeted manner to reduce the transportation cost.
Disclosure of Invention
The invention provides a GPS-based transportation cost intelligent accounting system and method, which are used for accurately classifying the cost in the transportation process, establishing a mathematical model by taking factors influencing the transportation cost as independent variables and the transportation cost as dependent variables, and determining influence factors of various expenses; the method solves the technical problem that the existing transportation cost accounting system can not establish a mathematical model by taking factors influencing the transportation cost as independent variables and the transportation cost as the independent variables to determine the influence factors of various expenses, thereby being convenient for pertinently taking measures to reduce the transportation cost.
The basic scheme provided by the invention is as follows: an intelligent transportation cost accounting system based on a GPS comprises a GPS module, a server, a remote control module and a display screen, wherein the GPS module is arranged on a transportation tool or important goods and is connected with the server through a wireless network; the server is connected with the remote control module through a wireless network; the server accounting step includes, S1: extracting expense data, S2: classifying the expense data based on the distance index by adopting a clustering analysis algorithm, S3: and (3) establishing a mathematical model by using the classified expense data as independent variables and the transportation cost as dependent variables through multivariate regression analysis, and determining influence factors of various expenses, S4: and outputting the data to a display screen.
The working principle of the invention is as follows: when goods generate certain cost in the transportation process, after the cost data is extracted by the server, classifying the received cost data by adopting a clustering analysis algorithm on the basis of distance indexes, establishing a mathematical model by using the classified cost data as independent variables and the transportation cost as dependent variables through multivariate regression analysis, determining influence factors of various costs, and then outputting a required chart; the GPS module transmits the real-time position of the vehicle to the remote control module through a wireless network. The invention has the advantages that: establishing a mathematical model by taking the classified expense data as independent variables and the transportation cost as dependent variables, and determining influence factors of various expenses, so that measures can be taken in a targeted manner to reduce the transportation cost; through the real-time positioning of the GPS module, the positions where various expenses occur can be quickly found so as to realize real-time supervision.
The GPS-based intelligent transportation cost accounting system adopts multivariate regression analysis to establish a mathematical model to determine the influence factors of various expenses, and can intuitively know the relative proportion of the various expenses through the influence factors, thereby facilitating the targeted measures to reduce the transportation cost. In addition, the real-time positioning of the GPS module can quickly find the positions where various expenses occur, thereby facilitating real-time supervision.
Further, the server accounting step also comprises feedback, processing and execution; the method comprises the following specific steps: s5: feedback, wherein the transportation cost calculated by the established mathematical model is used as feedback; s6: processing, namely determining the relative error between the transportation cost calculated by the mathematical model and the actual transportation cost; s7: executing, if the relative error is greater than the preset percentage, returning to the step S2; and if the relative error is smaller than the preset percentage, stopping iteration. When more data is spent, clustering on a large number of data set samples may lead to biased results, which may render the mathematical model determined by subsequent regression analysis less accurate. Therefore, when the relative error of the transportation cost calculated by the mathematical model determined by regression analysis exceeds a preset value, clustering needs to be performed again, and then regression is performed.
Further, step S2 adopts a k-means clustering algorithm (k-means clustering algorithm) when classifying the expense data; the method comprises the following specific steps: s21: inputting expense data; s22: randomly selecting K spending data as initial clustering centers; s23: assigning each object to the cluster center closest to it; s24: recalculating the clustering center; s25: if the convergence is achieved, outputting a clustering result; if not, the process returns to step S22. The k-means clustering algorithm is a classical iterative solution clustering analysis algorithm that can group a set of physical or abstract objects into classes composed of similar objects. The cost generated in the transportation process is caused by adopting the traditional classification method, so that the categories are various, even the situation that different categories are crossed mutually occurs, and the targeted statistics and analysis are not convenient. The objects in the same category separated by the k-means clustering algorithm have great similarity, and similar expenses are incorporated into the same category, which is beneficial to performing mathematical modeling on expense data subsequently, so as to perform statistics and analysis.
Further, the server converts the expense data into standard scores firstly and then performs linear regression; the method comprises the following specific steps: s31: all expense data variables were converted to standard scores, Z ═ X (X-X)0) (ii) S; wherein X is the original cost, X0Is the average of the original costs, S is the standard deviation of the original costs; s32: preliminarily setting a regression equation; s33: solving a regression coefficient; s34: obtaining a mathematical function of transportation cost and expense; s35: and outputting the data to a display screen. Since the unit of each expense data may not beLikewise, the magnitude of the coefficient before the variable does not account for the importance of the factor. For example, payroll expenditure is smaller than the regression coefficient obtained by using hundred yuan, but the influence degree of payroll expenditure on the transportation cost is not changed. Therefore, all the expense data are firstly converted into standard scores and then subjected to linear regression, and the obtained regression coefficient can accurately reflect the importance degree of the corresponding expense.
Further, the server is loaded with scanning software for acquiring expense data. Scanning is a device that converts graphic or image information into digital signals using electro-optical technology and digital processing technology. When the invoice and the contract are scanned, the invoice and the contract are not influenced by external light, and the accuracy and the reduction degree are high. When the camera is used for shooting, the influence of the external environment, particularly light rays, can be caused, and the shot picture has uneven light rays and lower restoration degree.
Further, the server is loaded with Optical Character Recognition (OCR) software for extracting spending data; the method comprises the following specific steps: s11: inputting and preprocessing pictures; s12: binaryzation; s13: image noise reduction; s14: correcting the inclination; s15: extracting character features; s16: comparing the databases; s17: comparing and identifying; s18: and outputting the result. OCR (Optical character recognition) refers to a process in which an electronic device examines a character printed on paper, determines its shape by detecting dark and light patterns, and then translates the shape into a computer text using a character recognition method. At present, the OCR technology is mature, a plurality of commercial software is available on the market, and the data of various invoices and contracts can be extracted quickly and accurately by adopting the OCR software.
Further, the remote control module adopts a network computer. Compared with the traditional PC and diskless station constructed network system, the overall security of the system is improved by the network computer; the cost of upgrading and updating is lower than that of a PC (personal computer) or a diskless station, only the server side is needed to upgrade, and the terminal does not need any upgrading; the data is completely stored in the server side, and a safe and efficient data protection mechanism is provided.
Further, still include the cloud camera, the cloud camera passes through the network and is connected with remote control module. The cloud camera is installed on the transport means, thereby can prevent to make false by real-time observation navigating mate's developments.
Further, the display screen comprises a first display screen and a second display screen; the first display screen is used for displaying various charts and data output by the server in real time; and the second display screen is used for displaying the position of the GPS, the transportation record, the vehicle track record and the video transmitted by the cloud camera in real time. The two display screens respectively display real-time graphs and positions and videos in the transportation process, so that specific data of expenses generated in the cargo transportation process can be quantitatively known, and specific positions and conditions of the expenses generated can be observed through the videos of the cloud cameras.
Drawings
FIG. 1 is a block diagram of a GPS-based transportation cost intelligent accounting system according to an embodiment of the present invention.
Detailed Description
The following is further detailed by the specific embodiments:
example 1
The embodiment of the GPS-based transportation cost intelligent accounting system is basically as shown in the attached figure 1, and comprises a GPS module, a cloud camera, a server, a remote control module, a first display screen and a second display screen.
The GPS module adopts a good GPSMAP 62sc positioner and is arranged on a transport vehicle or important goods; the real-time location of the vehicle is transmitted to the server over a wireless network by connecting to the server over the wireless network. The server adopts a Dell (DELL) R730/R7402U rack server, the acquired real-time position information is sent to the remote control module, and the remote control module adopts a server of a fusion Server 22 2288H V5 model. A Dell (DELL) R730/R7402U rack-mounted server is installed on a transport vehicle, and is loaded with Office Lens software developed by Microsoft and cloud OCR software developed by Xiamen cloud technology Limited, and is also loaded with SPSS software; the server is connected with the remote control module through a wireless network. The cloud camera adopts a Haokawav video DS-2DC6220IW-A type cloud camera, is installed in a cab of a transport tool, and sends shot real-time videos to a server of a remote control module through a wireless network.
When the goods generate certain expenses in the transportation process, invoices or contracts are issued to the parties. After the staff gets the invoice or contract, the Office Lens software on the Dall (DELL) R730/R7402U rack server is started to scan the corresponding invoice, contract and other data. After the Office Lens finishes scanning the materials such as the invoice, the contract and the like, OCR software is started to extract characters and data on the invoice and the contract. The concrete steps of extracting the invoice, the characters and the data on the contract by the OCR software are as follows: firstly, inputting scanned images of invoices, contracts and the like, carrying out image preprocessing on the images, and separating forms and character areas in the images. The difficulty of a feature extraction algorithm is reduced, and the identification precision is improved. And step two, binarization. Since an invoice or contract book may contain a color image, the amount of information contained in the color image is large. Before the characters in the picture are identified, the image is binarized to only contain black foreground information and white background information, so that the efficiency and the accuracy of identification processing are improved. And thirdly, reducing the noise of the image. Due to objective conditions in the transportation process and the limitation of the printing quality of the document, the image to be recognized is denoised according to the characteristics of noise before character recognition processing, and the accuracy of recognition processing is improved. And step four, correcting the inclination. The scanned invoice or contract has more or less inclination, so that the image direction is detected and corrected before character recognition processing, and the accuracy of recognition processing is improved. And step five, extracting character features. After the character image is thinned, the number and position of the stroke end points and cross points of the character are obtained, or the stroke segments are taken as the characteristics, and the comparison is carried out by matching with a special comparison method. Step six, comparing the database. After the characters are input and the characteristics are calculated, the characters are compared with a comparison database or a characteristic database. And step seven, comparing and identifying. The results are identified using various feature comparison methods. And step eight, outputting the result. And combining with Excel software, and generating the extracted data into a table form.
After the expense data is extracted, reading expense data in an Excel table form extracted by OCR software by SPSS software, and classifying the expense data by adopting a k-means clustering algorithm. The method comprises the following specific steps: step one, inputting expense data extracted by OCR software. And step two, randomly selecting K spending data as initial clustering centers. And step three, each object is assigned to the nearest cluster center. And step four, recalculating the clustering center. Step five, if convergence occurs, a clustering result is output; if not, returning to the step two.
After the expense data are classified, the SPSS software converts various clustered expense data into standard scores and then performs multivariate linear regression. The method comprises the following specific steps: converting all expense data variables into standard scores Z, wherein Z is (X-X0)/S; where X is the original cost, X0 is the average of the original costs, and S is the standard deviation of the original costs. And step two, preliminarily setting a regression equation. And step three, solving a regression coefficient. And step four, obtaining a mathematical function of the transportation cost and the expense. And step five, taking the transportation cost calculated by the established mathematical model as feedback. And step six, determining the relative error between the transportation cost cloud calculated by the mathematical model and the actual transportation cost. Step seven, if the relative error is larger than the preset percentage, executing the step two; and if the relative error is smaller than the preset percentage, stopping iteration. And step eight, outputting various charts. As shown in the sector diagram, the sector area represents the percentage of the total, and it is easy to display the size of each expense relative to the transportation cost.
After the various charts are output, the Dell (DELL) R730/R7402U rack server sends the various charts output by the SPSS software to a server of the type Hua fusion Server 2288H V5, and the server displays various charts and data in real time through a first display screen. The real-time position collected by the GPS module and the real-time video shot by the cloud camera are sent to a server of a fusion Server 2288H V5 model, and the server displays the position of GPS positioning, the transportation record, the vehicle track record and the video transmitted by the cloud camera in real time through a second display screen. The first display screen and the second display screen are all three-star S27R750QEC type display screens, the two display screens respectively display real-time charts and positions and videos in the transportation process, specific data of expenses generated in the cargo transportation process can be quantitatively known, and specific positions and conditions of the expenses generated can be observed through the videos of the cloud cameras.
Example 2
Compared with the embodiment 1, the difference is only that:
the server adopts SAS software, the software organically integrates data access, management, analysis and display, and the SAS server has the advantages of powerful function, complete statistical method, simple and convenient use, flexible operation, capability of providing an online help function and the like. The software can accurately classify the expense data and generate graphs and the like required by workers, so that visual data are provided for the workers. The first display screen and the second display screen can be combined into one display screen, one part displays graphs and data output by SAS software, and the other part displays real-time positioning and videos. Therefore, the staff can conveniently check the dynamic state of the transportation process at the same time, and the specific places where various expenses are generated can be determined.
Example 3
Compared with the embodiment 1, the difference is only that:
the pressure sensor is also arranged on the transport vehicle, acquires the change curve of the total weight of the transport vehicle and the transported goods along with time and sends the change curve to a Dall (DELL) R730/R7402U rack server.
Because can pass through speed reduction bank, unevenness road conditions on the way in transit, the condition that the driver suddenly braked, suddenly accelerated also can appear simultaneously. Therefore, due to the effect of the inertial force, the total weight of the transport vehicle and the transported goods acquired by the pressure sensor fluctuates with time to some extent. But this amplitude is usually small, such as within 1%; the duration is also short, such as a few minutes. When the Daire (DELL) R730/R7402U rack server detects that the peak of the total weight in the oscillation state exceeds a preset threshold (such as 2%), the Haekwover DS-2DC6220IW-A type cloud camera is controlled to be turned on to shoot videos. The fluctuation range of the total weight is too large and is probably not caused by inertia force in the driving process, and in this case, the situation that someone unloads the goods privately or the goods fall off the package can occur. Therefore, the cloud camera is opened at this time, so that the fact that the large fluctuation of the total weight is caused by that people unload the goods privately or the goods fall off the package is easily observed; thereby preventing the staff from being stolen by supervision and increasing extra cost.
The foregoing is merely an example of the present invention, and common general knowledge in the field of known specific structures and characteristics is not described herein in any greater extent than that known in the art at the filing date or prior to the priority date of the application, so that those skilled in the art can now appreciate that all of the above-described techniques in this field and have the ability to apply routine experimentation before this date can be combined with one or more of the present teachings to complete and implement the present invention, and that certain typical known structures or known methods do not pose any impediments to the implementation of the present invention by those skilled in the art. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (10)
1. GPS-based transportation cost intelligent accounting system is characterized in that: the system comprises a GPS module, a server, a remote control module and a display screen, wherein the GPS module is arranged on a transport tool or important goods and is connected with the server through a wireless network; the server is connected with the remote control module through a wireless network; the server accounting step includes, S1: extracting expense data, S2: classifying the expense data based on the distance index by adopting a clustering analysis algorithm, S3: and (3) establishing a mathematical model by using the classified expense data as independent variables and the transportation cost as dependent variables through multivariate regression analysis, and determining influence factors of various expenses, S4: and outputting the data to a display screen.
2. The GPS-based transportation cost intelligent accounting system according to claim 1, wherein: the accounting step of the server also comprises feedback, processing and execution; the method comprises the following specific steps: s5: feedback, wherein the transportation cost calculated by the established mathematical model is used as feedback; s6: processing, namely determining the relative error between the transportation cost calculated by the mathematical model and the actual transportation cost; s7: executing, if the relative error is greater than the preset percentage, returning to the step S2; and if the relative error is smaller than the preset percentage, stopping iteration.
3. The GPS-based transportation cost intelligent accounting system according to claim 2, wherein: step S2, a k-means clustering algorithm is adopted when the expense data are classified; the method comprises the following specific steps: s21: inputting expense data; s22: randomly selecting K spending data as initial clustering centers; s23: assigning each object to the cluster center closest to it; s24: recalculating the clustering center; s25: if the convergence is achieved, outputting a clustering result; if not, the process returns to step S22.
4. The GPS-based transportation cost intelligent accounting system according to claim 3, wherein: the server converts the expense data into standard scores and then performs linear regression; the method comprises the following specific steps: s31: converting all expense data variables into standard scores Z, Z ═ X0/S; where X is the original cost, X0 is the average of the original costs, and S is the standard deviation of the original costs; s32: preliminarily setting a regression equation; s33: solving a regression coefficient; s34: obtaining a mathematical function of transportation cost and expense; s35: and outputting the data.
5. The GPS-based transportation cost intelligent accounting system of claim 4, wherein: the server is loaded with scanning software for obtaining expense data.
6. The GPS-based transportation cost intelligent accounting system of claim 5, wherein: the server is loaded with Optical Character Recognition (OCR) software and used for extracting expense data; the method comprises the following specific steps: s11: inputting and preprocessing pictures; s12: binaryzation; s13: image noise reduction; s14: correcting the inclination; s15: extracting character features; s16: comparing the databases; s17: comparing and identifying; s18: and outputting the result.
7. The GPS-based transportation cost intelligent accounting system according to claim 6, wherein: the remote control module adopts a network computer.
8. The GPS-based transportation cost intelligent accounting system according to claim 7, wherein: the cloud camera is connected with the remote control module through a network.
9. The GPS-based transportation cost intelligent accounting system according to claim 8, wherein: the display screen comprises a first display screen and a second display screen; the first display screen is used for displaying various charts and data output by the server in real time; and the second display screen is used for displaying the position of the GPS, the transportation record, the vehicle track record and the video transmitted by the cloud camera in real time.
10. The GPS-based intelligent transportation cost accounting method is characterized by comprising the following steps: the method comprises the following steps:
s01: the expense data is extracted and the cost data is,
s02: a clustering analysis algorithm is used to classify the spending data based on the distance index,
s03: converting the expense data into standard scores, establishing a mathematical model by using the classified expense data as independent variables and the transportation cost as dependent variables through multivariate regression analysis, and determining influence factors of various expenses;
s04: taking the transportation cost calculated by the established mathematical model as feedback;
s05: determining relative errors of the transportation cost calculated by the mathematical model and the actual transportation cost;
s06: if the relative error is greater than the preset percentage, returning to the step S02; if the relative error is smaller than the preset percentage, stopping iteration;
s07: outputting various charts;
s08: the chart is displayed through the screen.
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