Electronic bill track data filtering method, device and equipment and storage medium
By calculating the flow distance of electronic manifest trajectory data and removing abnormal location information, combined with preprocessing and erroneous data removal, the problem of misoperation in electronic manifest trajectory data filtering was solved, and the accuracy and reliability of the data were improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHENZHEN SHUYAN JINHAN INTELLIGENT TECH CO LTD
- Filing Date
- 2024-10-12
- Publication Date
- 2026-04-17
AI Technical Summary
Existing electronic manifest trajectory data filtering methods cannot effectively improve the accuracy and reliability of the data, especially since accidental deletion due to misoperation is prone to occur during data collection, transmission and storage.
By acquiring electronic manifest trajectory data, calculating the flow distance and comparing it with the preset target distance, eliminating location information where the flow distance exceeds the target distance, and then performing preprocessing, deduplication, and erroneous data removal on this basis, and finally performing visual verification to ensure the accuracy and reliability of the data.
This effectively avoids the accidental deletion of normal data, improves the accuracy and reliability of filtering electronic manifest trajectory data, and ensures the integrity and availability of the data.
Smart Images
Figure CN121882853A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data filtering, and in particular to a method, apparatus, device, and storage medium for filtering electronic manifest trajectory data. Background Technology
[0002] With the continuous development and popularization of logistics, distribution, transportation and other fields, the application of electronic forms is becoming more and more widespread.
[0003] The application of electronic manifest systems has led to the accumulation of a large amount of electronic manifest tracking data in enterprises and logistics sectors. However, errors in the collection, transmission, and storage of this data, such as accidental deletion, have affected its accuracy and reliability. Traditional methods for filtering electronic manifest tracking data are mainly based on preset rules or models, such as statistical or machine learning methods. However, these methods still cannot solve the problems of accuracy and reliability.
[0004] Therefore, finding a method for filtering electronic manifest trajectory data that improves accuracy and reliability has become an urgent problem for those skilled in the art. Summary of the Invention
[0005] This invention aims to at least partially address one of the technical problems in the related art. Therefore, one object of this invention is to provide a method for filtering electronic manifest trajectory data that improves accuracy and reliability.
[0006] The technical solution adopted in this invention is:
[0007] In a first aspect, the present invention provides a method for filtering electronic manifest trajectory data, the method comprising:
[0008] Acquire electronic manifest trajectory data, which is data generated based on the electronic manifest and the target vehicle's movement trajectory. The electronic manifest trajectory data includes the movement position information of the target vehicle in the movement trajectory.
[0009] Based on the flow location information, the flow distance of the target vehicle is calculated, where the flow distance is the distance the target vehicle travels from the previous flow location to the next flow location;
[0010] The flow distance is compared with the preset target distance to obtain the comparison result;
[0011] If the comparison result shows that the flow distance is greater than the target distance, then the flow position information corresponding to the next flow position will be discarded.
[0012] The electronic manifest trajectory data filtering method also includes the following steps before acquiring the electronic manifest trajectory data:
[0013] The electronic manifest trajectory data is preprocessed to obtain the processed electronic manifest trajectory data.
[0014] The electronic manifest trajectory data filtering method also includes the following steps before acquiring the electronic manifest trajectory data:
[0015] The electronic manifest trajectory data is deduplicated to obtain the deduplicated electronic manifest trajectory data.
[0016] The deduplicated electronic manifest trajectory data is then processed to remove erroneous data, resulting in the removed electronic manifest trajectory data.
[0017] The electronic manifest trajectory data filtering method further includes, after removing the flow position information corresponding to the next flow position:
[0018] The data after rejection is output in a structured manner. The data after rejection is the data obtained by rejecting the flow position information corresponding to the next flow position.
[0019] The electronic manifest trajectory data filtering method further includes, after removing the flow position information corresponding to the next flow position:
[0020] Visualize and verify the data after it has been removed.
[0021] Secondly, the present invention provides an electronic manifest trajectory data filtering device, the electronic manifest trajectory data filtering device comprising:
[0022] The acquisition module is used to acquire electronic manifest trajectory data, wherein the electronic manifest trajectory data is data generated based on the electronic manifest and the target vehicle's movement trajectory, and the electronic manifest trajectory data includes the movement position information of the target vehicle in the movement trajectory;
[0023] The calculation module is used to calculate the flow distance of the target vehicle based on the flow location information, where the flow distance is the distance the target vehicle travels from the previous flow location to the next flow location;
[0024] The comparison module is used to compare the flow distance with the preset target distance and obtain the comparison result;
[0025] The location information rejection module is used to reject the flow location information corresponding to the next flow location if the comparison result shows that the flow distance is greater than the target distance.
[0026] The electronic manifest trajectory data filtering device also includes:
[0027] The preprocessing module is used to preprocess the electronic manifest trajectory data to obtain the processed electronic manifest trajectory data.
[0028] The electronic manifest trajectory data filtering device also includes:
[0029] The deduplication module is used to deduplicatize the electronic manifest trajectory data to obtain deduplicated electronic manifest trajectory data.
[0030] The error data removal module is used to remove error data from the deduplicated electronic manifest trajectory data, resulting in the removed electronic manifest trajectory data.
[0031] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described electronic manifest trajectory data filtering method.
[0032] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described electronic manifest trajectory data filtering method.
[0033] The beneficial effects of this invention are as follows:
[0034] In the aforementioned electronic manifest trajectory data filtering method, apparatus, computer equipment, and readable storage medium, the data generated by the target vehicle's movement trajectory based on the electronic manifest is automatically acquired first. This electronic manifest trajectory data includes the movement position information within the target vehicle's movement trajectory. Then, based on the obtained movement position information, the distance the target vehicle travels from one movement position to the next is calculated. Next, the movement distance is compared with a preset target distance to obtain a comparison result. Finally, if the comparison result shows that the movement distance is greater than the target distance, the movement position information corresponding to the next movement position is removed. Since a movement distance greater than the target distance indicates an abnormality in the target vehicle, the removed information must be abnormal movement position information. This avoids the erroneous manipulation of electronic manifest trajectory data found in existing technologies, such as mistakenly deleting correct data where the target vehicle's speed is zero under normal parking conditions, thus improving the accuracy and reliability of electronic manifest trajectory data filtering. Attached Figure Description
[0035] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0036] Figure 1This is a schematic diagram of an application environment for the electronic manifest trajectory data filtering method in one embodiment of the present invention;
[0037] Figure 2 This is a flowchart of an electronic manifest trajectory data filtering method according to an embodiment of the present invention;
[0038] Figure 3 This is a flowchart of data cleaning in an electronic manifest trajectory data filtering method according to an embodiment of the present invention;
[0039] Figure 4 This is a schematic diagram of an electronic manifest trajectory data filtering device according to an embodiment of the present invention;
[0040] Figure 5 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation
[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0042] The method provided in this application can be applied to, for example... Figure 1 In this application environment, the environment includes a server and a client. The client communicates with the server via a wired or wireless network. The client can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server can be implemented as a standalone server or a server cluster consisting of multiple servers. The client collects the movement location information of the target vehicle's trajectory and uploads this information to the server. The server uses this movement location information to calculate the movement distance and remove abnormal movement location information.
[0043] In one embodiment, such as Figure 2 As shown, an electronic manifest trajectory data filtering method is provided, which is applied to... Figure 1 Taking the server-side as an example, the explanation includes the following steps:
[0044] S10. Obtain electronic manifest trajectory data;
[0045] Specifically, in the construction waste industry, in order to improve the supervision of construction waste, more and more places have introduced regulations. These regulations require that electronic manifests be present before construction sites and disposal sites can be accessed, thus giving rise to the electronic manifest system.
[0046] The so-called electronic manifest refers to the cross-verification of big data throughout the entire process of construction waste from its source to transportation, transfer, and disposal, forming a closed-loop information carrier. By creating associated documents from this information carrier, an electronic manifest is formed.
[0047] For example, the activity of removing construction waste from its source, delivering it to a transportation unit, and moving it to a disposal site requires monitoring of the process. This process is then documented using a multi-part electronic manifest for construction waste. It should be noted that a multi-part electronic manifest means that one electronic manifest contains multiple copies. For example, one electronic manifest may have a first copy, a second copy, and a third copy, which are distinguished by different colors.
[0048] To illustrate the flow of electronic forms clearly, an example is given below:
[0049] First, the electronic manifest is initiated. Specifically, the on-site management personnel of the construction waste generating unit scan the unique QR code of the transport vehicle, select the excavation site, and verify the automatically linked disposal permit information, vehicle information, and destination information. They then fill in the type of construction waste, and the on-site management personnel sign and confirm to generate the first copy (Copy A) of the electronic manifest. The generating unit must complete and process one electronic construction waste manifest for each transfer of construction waste. If different generating units use the same transport vehicle, they must complete and process separate electronic construction waste manifests.
[0050] Then, the electronic manifest is circulated. Specifically, based on the information in the disposal permit and pass, the driver transports the goods to the designated disposal site according to the prescribed route. Upon arrival, the driver signs to confirm receipt, generating the second copy (B copy) of the electronic manifest. The system automatically matches the origin, route, and destination data and associates them with the electronic manifest for easy receipt by the disposal site.
[0051] Next, the electronic manifest is closed. Specifically, after the transport vehicle arrives at the disposal site, the on-site management personnel scan the code or verify the electronic manifest information via PC, checking details such as the type of construction waste and the quantity received. For example, disposal units that have configured and are using video surveillance, number recognition, and vehicle / cargo weighing detection equipment should prioritize using the equipment information. In special circumstances, manual input and confirmation are possible. After the on-site management personnel sign and confirm, the third copy (C copy) of the electronic manifest is formed. Once the electronic manifest information is completely closed, the electronic manifest is closed.
[0052] Finally, the electronic manifests are stored. After the electronic manifest information is generated, it is entered into the manifest information database, and functions such as electronic manifest listing, electronic manifest trajectory playback, electronic manifest query, electronic manifest data statistics, and electronic manifest comparison analysis are implemented.
[0053] The so-called electronic manifest trajectory data refers to the data obtained from the trajectory generated by the target vehicle during its movement, based on the electronic manifest. It should be noted that the electronic manifest trajectory data includes the vehicle's position information within its movement trajectory, as well as information such as timestamps. The position can be a start point, destination, etc.
[0054] Specifically, the location information refers to the latitude and longitude of the trajectory generated by the target vehicle during its movement. For example, if the starting point of the movement of a construction waste transport vehicle is the Tencent Building in Shenzhen, and it passes through the Xili Metro Station in Shenzhen during its movement, and the latitude and longitude of the Tencent Building in Shenzhen are "longitude coordinates 2560688.25, latitude coordinates 12684000.92", then "longitude coordinates 2560688.25, latitude coordinates 12684000.92" is one of the location information of the movement.
[0055] Specifically, the electronic manifest trajectory data is collected by the client and then transmitted to the server via the network. When the server receives the electronic manifest trajectory data, it stores it in the trajectory database so that it can be retrieved at any time.
[0056] In order to access the electronic manifest trajectory data, the server obtains the storage path of the electronic manifest trajectory data, and then extracts the electronic manifest trajectory data based on the storage path.
[0057] It should be noted that the client typically uses location sensors to collect the trajectory data of the electronic manifest, such as GPS or BeiDou positioning. The trajectory database can be a MySQL database or an Oracle database, etc. The specific content of the trajectory database can be set according to the actual application, and there are no restrictions here.
[0058] S20. Calculate the movement distance of the target vehicle based on the movement location information;
[0059] In step S10, after the server obtains the movement location information, it parses the movement location information for each location and analyzes the distance the target vehicle travels from one location to the next, based on the chronological order. This can be understood as follows: From a chronological perspective, as the target vehicle moves from one location to the next, the pre-collected movement location information is used to calculate the distance between the two locations. For example, using existing formulas for calculating the distance between two points on Earth, along with the latitude and longitude of those two points, the distance can be calculated.
[0060] It should be noted that the formulas for calculating the distance between two points on Earth based on latitude and longitude are existing technologies and will not be elaborated upon here.
[0061] Another approach to calculate the distance from one flow position to the next can be used. This can be understood as follows: the client collects the real-time speed of the target vehicle, then collects the time point at which the target vehicle moves from the previous flow position to the next flow position. From this time point, the total time taken for the target vehicle to move from the previous flow position to the next flow position is calculated. Then, from this time and the sum of the real-time speeds within that time, the average speed of the target vehicle moving from the previous flow position to the next flow position is calculated. Finally, the average speed is multiplied by the time to obtain the distance the target vehicle moves from the previous flow position to the next flow position.
[0062] S30. Compare the flow distance with the preset target distance to obtain the comparison result, that is, determine whether the flow distance is greater than the preset target distance;
[0063] Specifically, after the server calculates the flow distance of the target vehicle in step S20, it retrieves the target distance stored in the distance database in advance and compares the flow distance with the target distance. That is, it determines whether the flow distance is greater than the preset target distance by calculating the difference between the flow distance and the target distance and obtaining a positive or negative number or zero comparison result. If the comparison result is positive, it means that the flow distance is greater than the target distance. If the positive number is greater than the preset value, it means that the flow location information corresponding to the next flow location has an anomaly. Therefore, the server executes step S40, that is, the server removes the flow location information corresponding to the next flow location.
[0064] For example, the GPS sensor on the target vehicle collects location information every minute. The average speed of the target vehicle is 60 km / h, and the error range is 10%. Normally, the distance between two collection points should not exceed 1 kilometer. The distance from point A to point B is 1.1 kilometers, which is within the normal error range. The distance from point B to point C is 0.9 kilometers, which is also within the normal error range. The distance from point C to point D is 30 kilometers, which is far more than 1 kilometer. Therefore, it can be determined that the location information of point D is abnormal, and the server will remove the location information of point D.
[0065] Furthermore, if the comparison result is zero or negative, it means that the flow distance is equal to or less than the target distance, indicating that the flow location information corresponding to the next flow location is normal. Thus, the server executes step S50, that is, the server saves and stores the flow location information corresponding to the next flow location in the trajectory database so that it can be called at any time when needed.
[0066] It should be noted that the distance database can be a MySQL database or an Oracle database, etc. The specific content of the distance database can be set according to the actual application, and there are no restrictions here.
[0067] First, the system automatically acquires data based on the electronic manifest's trajectory of the target vehicle, including its position information. Then, based on this position information, it calculates the distance the target vehicle travels from one position to the next. Next, it compares this distance with a preset target distance. Finally, if the distance is greater than the target distance, the position information corresponding to the next position is removed. Since a greater distance indicates an anomaly in the target vehicle, the removed information is always abnormal. This avoids the erroneous manipulation of electronic manifest trajectory data common in existing technologies, such as mistakenly deleting correct data where the target vehicle's speed is zero under normal parking conditions. This improves the accuracy and reliability of electronic manifest trajectory data filtering.
[0068] Furthermore, prior to step S10, the electronic manifest trajectory data filtering method also includes:
[0069] Specifically, the server preprocesses the electronic manifest trajectory data to obtain processed electronic manifest trajectory data. That is, the server standardizes and normalizes the electronic manifest trajectory data, such as performing format conversion, outlier handling, unit unification, data format unification, timestamp format unification, and / or location coordinate conversion, thereby ensuring the consistency and usability of the electronic manifest trajectory data and facilitating the calculation in step S20.
[0070] In one embodiment, such as Figure 3 As shown, in Figure 2 Based on this, before step S10, the electronic manifest trajectory data filtering method also includes data cleaning, as follows:
[0071] S60. Perform deduplication processing on the electronic manifest trajectory data to obtain the deduplicated electronic manifest trajectory data;
[0072] S70. Perform error data removal processing on the deduplicated electronic manifest trajectory data to obtain the removed electronic manifest trajectory data.
[0073] Specifically, the initial electronic manifest trajectory data collected contains some noise. In order to obtain clean electronic manifest trajectory data, the server uses clustering algorithms such as K-means to perform cluster analysis on the standardized and normalized electronic manifest trajectory data, identify and remove duplicate electronic manifest trajectory data, and thus obtain non-duplicated electronic manifest trajectory data.
[0074] Next, the server uses algorithms such as decision tree, random forest, and Z-score to classify and analyze the electronic manifest trajectory data, automatically identify and label erroneous electronic manifest trajectory data, and then remove the labeled erroneous electronic manifest trajectory data to obtain the removed electronic manifest trajectory data, thereby ensuring the correctness of the electronic manifest trajectory data.
[0075] It should be noted that clustering analysis of electronic manifest trajectory data using clustering algorithms such as K-means, and classification analysis of electronic manifest trajectory data using algorithms such as decision trees, random forests, and Z-scores are all existing technologies and will not be elaborated on here.
[0076] Furthermore, filtering conditions can be set according to actual needs. For example, electronic manifest trajectory data within a specific time period can be filtered based on timestamps; electronic manifest trajectory data that conforms to a geographical range can be filtered based on movement location information. These filtering conditions can also be time ranges, geographical ranges, etc.
[0077] By setting flexible filtering conditions, the electronic manifest trajectory data that meets the requirements can be further filtered out.
[0078] In one embodiment, in Figure 2 or Figure 3 Based on this, after removing the flow position information corresponding to the next flow position, the electronic manifest trajectory data filtering method also includes:
[0079] Specifically, to facilitate the analysis and utilization of electronic manifest trajectory data, after removing the flow location information corresponding to the next flow location, the server outputs the removed flow location information in a structured manner. For example, the server outputs the flow location information using database tables, Excel spreadsheets, etc., thereby improving the sustainable usability of the flow location information. Therefore, the sustainable usability of electronic manifest trajectory data is improved.
[0080] It should be noted that the data after elimination is the data obtained by removing the flow position information corresponding to the next flow position.
[0081] Furthermore, to ensure the intuitiveness and accuracy of electronic manifest trajectory data, after removing the flow position information corresponding to the next flow position, the electronic manifest trajectory data filtering method also includes:
[0082] Specifically, the server performs visual verification on the removed data. That is, the server uses visualization methods such as histograms, pie charts, bar charts, scatter plots, and / or line charts to output the removed flow location information, thereby improving the intuitiveness of the flow location information and the electronic manifest trajectory data. Then, manual judgment is used to determine whether the removed flow location information is correct, thus ensuring the accuracy of the removed flow location information and therefore ensuring the accuracy of the electronic manifest trajectory data.
[0083] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
[0084] Please see Figure 4 , Figure 4 This is a schematic diagram of an embodiment of the electronic manifest trajectory data filtering device of the present invention. Figure 4 As shown, the device includes: an acquisition module 11, a calculation module 12, a comparison module 13, a location information removal module 14, a preprocessing module 15, a deduplication module 16, and an error data removal module 17. Specifically:
[0085] The acquisition module 11 is used to acquire electronic manifest trajectory data, wherein the electronic manifest trajectory data is data generated based on the electronic manifest and the target vehicle flow trajectory, and the electronic manifest trajectory data includes the flow position information in the target vehicle flow trajectory;
[0086] The calculation module 12 is used to calculate the flow distance of the target vehicle based on the flow location information, wherein the flow distance is the distance generated by the target vehicle from the previous flow location to the next flow location;
[0087] Comparison module 13 is used to compare the flow distance with the preset target distance to obtain the comparison result;
[0088] The location information rejection module 14 is used to reject the flow location information corresponding to the next flow location if the comparison result is that the flow distance is greater than the target distance.
[0089] Furthermore, the electronic manifest trajectory data filtering device also includes:
[0090] The preprocessing module 15 is used to preprocess the electronic manifest trajectory data to obtain the processed electronic manifest trajectory data.
[0091] Furthermore, the electronic manifest trajectory data filtering device also includes:
[0092] The deduplication module 16 is used to deduplicatize the electronic manifest trajectory data to obtain deduplicated electronic manifest trajectory data.
[0093] Error data removal module 17 is used to remove error data from the deduplicated electronic manifest trajectory data to obtain the removed electronic manifest trajectory data.
[0094] Specifically, the working methods of each module in this embodiment have been described in detail in Embodiment 1, and will not be repeated here.
[0095] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes a non-volatile readable storage medium and internal memory. The non-volatile readable storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile readable storage medium. The database stores data involved in the method. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for generating a waste management system.
[0096] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method described in the above embodiments, for example... Figure 2 Steps S10 to S50 are shown.
[0097] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When executed by a processor, the computer program implements the methods described in the waste management system generation method embodiments. Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
[0098] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.
[0099] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.
Claims
1. An electronic single document track data filtering method, characterized by, The electronic manifest trajectory data filtering method includes: The electronic manifest trajectory data is obtained, wherein the electronic manifest trajectory data is data generated based on the electronic manifest and the target vehicle's flow trajectory, and the electronic manifest trajectory data includes the flow position information in the target vehicle's flow trajectory; Based on the flow location information, the flow distance of the target vehicle is calculated, wherein the flow distance is the distance the target vehicle travels from the previous flow location to the next flow location; The flow distance is compared with the preset target distance to obtain the comparison result; If the comparison result is that the flow distance is greater than the target distance, then the flow position information corresponding to the next flow position is discarded.
2. The electronic manifest trajectory data filtering method as described in claim 1, characterized in that, Before acquiring the electronic manifest trajectory data, the electronic manifest trajectory data filtering method further includes: The electronic manifest trajectory data is preprocessed to obtain processed electronic manifest trajectory data.
3. The electronic manifest trajectory data filtering method as described in claim 1, characterized in that, Before acquiring the electronic manifest trajectory data, the electronic manifest trajectory data filtering method further includes: The electronic manifest trajectory data is deduplicated to obtain the deduplicated electronic manifest trajectory data. The deduplicated electronic manifest trajectory data is then processed to remove erroneous data, resulting in the removed electronic manifest trajectory data.
4. The electronic manifest trajectory data filtering method as described in any one of claims 1 to 3, characterized in that, After removing the flow position information corresponding to the next flow position, the electronic manifest trajectory data filtering method further includes: The data after rejection is output in a structured manner, wherein the data after rejection is the data obtained in the step of rejecting the flow position information corresponding to the next flow position.
5. The electronic manifest trajectory data filtering method as described in claim 4, characterized in that, After removing the flow position information corresponding to the next flow position, the electronic manifest trajectory data filtering method further includes: The data after the removal is then visually verified.
6. An electronic manifest trajectory data filtering device, characterized in that, The electronic manifest trajectory data filtering device includes: The acquisition module is used to acquire the electronic manifest trajectory data, wherein the electronic manifest trajectory data is data generated based on the electronic manifest and the target vehicle flow trajectory, and the electronic manifest trajectory data includes the flow position information in the target vehicle flow trajectory; The calculation module is used to calculate the flow distance of the target vehicle based on the flow location information, wherein the flow distance is the distance generated by the target vehicle flowing from the previous flow location to the next flow location; The comparison module is used to compare the flow distance with a preset target distance to obtain a comparison result; The location information elimination module is used to eliminate the flow location information corresponding to the next flow location if the comparison result is that the flow distance is greater than the target distance.
7. The electronic manifest trajectory data filtering device as described in claim 6, characterized in that, The electronic manifest trajectory data filtering device also includes: The preprocessing module is used to preprocess the electronic manifest trajectory data to obtain processed electronic manifest trajectory data.
8. The electronic manifest trajectory data filtering device as described in claim 6 or 7, characterized in that, The electronic manifest trajectory data filtering device also includes: The deduplication module is used to deduplicatize the electronic manifest trajectory data to obtain deduplicated electronic manifest trajectory data. The error data removal module is used to remove error data from the deduplicated electronic manifest trajectory data to obtain the removed electronic manifest trajectory data.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the electronic manifest trajectory data filtering method as described in any one of claims 1 to 5.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the electronic manifest trajectory data filtering method as described in any one of claims 1 to 5.