Carbon credit data statistical method and system based on enterprise travel

Abstract

The application relates to a carbon credit data statistics method and system based on enterprise travel, which comprises the following steps: acquiring actual travel path data; acquiring current vehicle initial fuel consumption based on the actual travel distance and actual consumed oil quantity, generating an evaluable fuel consumption data set, and the evaluable fuel consumption data set comprises multiple current evaluable vehicle fuel consumptions; then generating a current carbon credit evaluation value, acquiring low-carbon management behaviors of drivers and passengers of an actual travel supervision vehicle, and generating a current vehicle travel carbon credit value; finally acquiring exchangeable electricity data of each preset supervision function department in a current carbon credit supervision enterprise, and generating a current enterprise energy consumption summary carbon credit. The application avoids the influence of inaccurate statistics caused by the wear of vehicle fuel consumption statistics devices in vehicles due to different vehicle service life and vehicle consumption, thereby avoiding the problem of inaccurate fuel consumption statistics, and realizes accurate, efficient and reliable acquisition of carbon credit data statistics of enterprise travel.

Description

Technical Field

[0001] This application relates to the field of carbon credit statistics technology, and in particular to a method and system for collecting carbon credit data based on corporate travel. Background Technology

[0002] Carbon credits refer to carbon dioxide emission allowances established by the European Union. Businesses or individuals can eliminate their carbon footprint by purchasing carbon credits. Carbon credits are a concrete manifestation of a person's low-carbon and environmentally friendly lifestyle. Users can obtain carbon credits in various ways, such as by scanning a QR code to ride public transportation through a specific app. Alternatively, after completing environmentally friendly activities such as online registration, walking donations, and paying utility bills online, the carbon account will calculate the reduced carbon emissions. These reduced emissions will then be exchanged for carbon credits at a certain ratio. These methods also allow users to earn carbon credits.

[0003] However, current technologies assess and calculate carbon credits for each market directly based on different fuel-powered vehicles. For car manufacturers, if the fuel consumption of their vehicles is below the prescribed standard, they directly accumulate positive carbon credits; otherwise, they accumulate negative carbon credits. However, after the fuel consumption of vehicles produced by car manufacturers is sold to different user companies, the carbon credits of the user companies are generally calculated based on the original fuel consumption of the vehicle. This has problems: firstly, the fuel consumption of a vehicle is constantly changing, affected by the vehicle's age and wear and tear; secondly, it is affected by the users, leading to inaccurate calculations of the user companies' travel carbon credits.

[0004] Furthermore, the carbon credit statistics related to the travel of car-using companies are actually calculated using the original fuel consumption statistics of the vehicles. However, the fuel consumption statistics of different vehicles are different, which is prone to errors. Moreover, since the types and varieties of vehicles owned by a car-using company are different, using the original fuel consumption statistics to calculate carbon credits is more likely to result in inaccurate carbon credit data. Summary of the Invention

[0005] Therefore, it is necessary to provide a method and system for statistical analysis of carbon credits based on corporate travel that can improve data processing efficiency in response to the above-mentioned technical problems.

[0006] The technical solution of this invention is as follows:

[0007] A method for statistical analysis of carbon credits based on corporate travel, the method comprising:

[0008] The system acquires actual travel route data of vehicles under the current carbon credit supervision enterprise within a preset specific time period. Each vehicle has multiple actual travel route data points, including actual travel distance and actual fuel consumption. Based on the actual travel distance and actual fuel consumption, the system obtains the initial fuel consumption of the current vehicle. This initial fuel consumption is then subjected to fuel consumption quality inspection and screening, and an evaluable fuel consumption dataset is generated after screening. This evaluable fuel consumption dataset includes multiple currently evaluable vehicle fuel consumption values. Carbon credit assessment is performed on the fuel consumption of each currently evaluable vehicle, and a current carbon credit assessment estimate is generated for each. The system also acquires the low-carbon management behavior of the drivers and passengers of the vehicles under the actual travel supervision, and generates a current vehicle trip carbon credit value based on the low-carbon management behavior and each current carbon credit assessment estimate. Finally, the system acquires the redeemable electricity consumption data of each preset regulatory functional department within the current carbon credit supervision enterprise, generates a current electricity consumption carbon credit value based on the redeemable electricity consumption data, and generates a current enterprise energy consumption summary carbon credit based on the current vehicle trip carbon credit value and the current electricity consumption carbon credit value.

[0009] Furthermore, based on the actual travel distance and actual fuel consumption, the initial fuel consumption of the current vehicle is obtained. This initial fuel consumption is then subjected to a fuel consumption quality inspection and screening process. After screening, an evaluable fuel consumption dataset is generated, which includes multiple current evaluable vehicle fuel consumption figures; specifically, it includes:

[0010] The initial fuel consumption of the current vehicle is obtained based on the actual travel distance and actual fuel consumption, and the actual vehicle type of the monitored vehicle is also obtained. The standard rated fuel consumption corresponding to the actual vehicle type is obtained. The standard qualified fuel consumption range corresponding to the standard rated fuel consumption is obtained, and the standard qualified fuel consumption range includes multiple refined fuel consumption ranges. The initial fuel consumption of the current vehicle is compared with the standard qualified fuel consumption ranges, and a current fuel consumption level is generated based on the comparison result. Each refined fuel consumption range corresponds to an actual standard fuel consumption level. When the initial fuel consumption of the current vehicle falls within a refined fuel consumption range, the current fuel consumption level of the current vehicle is the actual standard fuel consumption level corresponding to the refined fuel consumption range. The initial fuel consumption of the current vehicle is filtered based on the current fuel consumption level, and the currently evaluable vehicle fuel consumption is selected. An evaluable fuel consumption dataset is generated based on each of the currently evaluable vehicle fuel consumptions.

[0011] Furthermore, carbon credit assessments are performed on the fuel consumption of each of the currently assessable vehicles, and current carbon credit assessment estimates are generated for each. The low-carbon management behavior of drivers and passengers in the monitored vehicles is obtained, and a current vehicle trip carbon credit value is generated based on the low-carbon management behavior and the current carbon credit assessment estimates. Specifically, this includes:

[0012] The process involves determining the actual proportion of each currently assessable vehicle fuel consumption level within its corresponding refined fuel consumption range; generating a current carbon credit assessment value for each currently assessable vehicle fuel consumption based on its actual proportion; obtaining the low-carbon management behavior of drivers and passengers in monitored vehicles, including data on paper ticket savings and in-vehicle air conditioning energy saving; generating a driver's low-carbon credit based on the paper ticket savings and in-vehicle air conditioning energy saving data; and generating a current vehicle trip carbon credit value based on the driver's low-carbon credit and each of the current carbon credit assessment values.

[0013] Furthermore, the system acquires actual travel route data of vehicles monitored by current carbon credit regulatory enterprises within a preset specific time period. Each monitored vehicle has multiple actual travel route data points, and each actual travel route data point includes the actual travel distance and actual fuel consumption, specifically including:

[0014] The system acquires a successful installation command for a positioning communication module installed on the actual travel monitoring vehicle; generates a positioning activation command based on the successful installation command and sends the activation command to the positioning communication module, which is used to activate the positioning communication module; acquires a successful fuel level detection command for a fuel level detection module installed on the actual travel monitoring vehicle; generates a fuel level detection activation command based on the successful fuel level detection command and sends the activation command to the fuel level detection module, which is used to activate the fuel level detection module; acquires the actual travel distance based on the positioning communication module and the actual fuel consumption based on the fuel level detection module, wherein the combination of the actual travel distance and the actual fuel consumption constitutes the actual travel route data.

[0015] Furthermore, the process involves acquiring the redeemable electricity consumption data of each preset regulatory functional department within the current carbon credit supervision enterprise, generating the current electricity consumption carbon credit value based on the redeemable electricity consumption data, and generating the current enterprise's total energy consumption carbon credit based on the current vehicle trip carbon credit value and the current electricity consumption carbon credit value. Specifically, this includes:

[0016] The system obtains the rated statistical electricity consumption of each preset regulatory functional department within the current carbon credit monitoring enterprise; calculates the initial actual electricity consumption of each preset regulatory functional department within the current carbon credit monitoring enterprise; generates actual electricity savings based on the rated statistical electricity consumption and the initial actual electricity consumption; obtains peak electricity consumption during peak hours based on the initial actual electricity consumption; generates peak allowable electricity consumption based on the difference between the peak electricity consumption and the preset standard electricity consumption; generates redeemable electricity data based on the actual electricity savings and the peak allowable electricity consumption, and generates the current electricity carbon credit value based on the redeemable electricity data; and generates the current enterprise's total energy consumption carbon credit based on the current vehicle trip carbon credit value and the current electricity carbon credit value.

[0017] Furthermore, a carbon credit data statistics system based on corporate travel, the system comprising:

[0018] The actual route acquisition module is used to acquire the actual travel route data of the actual travel monitoring vehicles of the current carbon credit monitoring enterprise within a preset specific time period. Each actual travel monitoring vehicle has multiple actual travel route data, and each actual travel route data includes the actual travel distance and the actual fuel consumption.

[0019] The fuel consumption quality inspection and screening module is used to obtain the current vehicle's initial fuel consumption based on the actual travel distance and actual fuel consumption, perform fuel consumption quality inspection and screening on the current vehicle's initial fuel consumption, and generate an evaluable fuel consumption dataset after the screening is completed. The evaluable fuel consumption dataset includes multiple current evaluable vehicle fuel consumption data.

[0020] The carbon credit assessment generation module is used to assess the fuel consumption of each of the currently assessable vehicles and generate a current carbon credit assessment estimate, obtain the low-carbon management behavior of the drivers and passengers of the actual travel monitored vehicles, and generate the current vehicle trip carbon credit value based on the low-carbon management behavior and each of the current carbon credit assessment estimates.

[0021] The summary points generation module is used to obtain the redeemable electricity consumption data of each preset regulatory functional department in the current carbon points supervision enterprise, generate the current electricity consumption carbon points value based on the redeemable electricity consumption data, and generate the current enterprise's energy consumption summary carbon points based on the current vehicle trip carbon points value and the current electricity consumption carbon points value.

[0022] Furthermore, the fuel consumption quality inspection and screening module also includes:

[0023] The vehicle type acquisition module is used to obtain the initial fuel consumption of the current vehicle based on the actual travel distance and actual fuel consumption, and to obtain the actual vehicle type of the actual travel monitored vehicle.

[0024] A rated fuel consumption acquisition module is used to acquire a standard rated fuel consumption corresponding to the actual vehicle type based on the actual vehicle type.

[0025] The fuel consumption range refinement module is used to obtain the standard qualified fuel consumption range corresponding to the standard rated fuel consumption based on the standard rated fuel consumption. The standard qualified fuel consumption range includes multiple refined fuel consumption ranges.

[0026] The fuel consumption range comparison module is used to compare the current initial fuel consumption of the vehicle with the standard qualified fuel consumption range, and generate the current fuel consumption level of the current initial fuel consumption based on the comparison result. Each refined fuel consumption range corresponds to an actual standard fuel consumption level. When the current initial fuel consumption of the vehicle is within a refined fuel consumption range, the current fuel consumption level of the current initial fuel consumption is the actual standard fuel consumption level corresponding to the refined fuel consumption range.

[0027] The fuel consumption assessment generation module is used to filter the initial fuel consumption of the corresponding current vehicle according to the current fuel consumption level, filter out the current assessable vehicle fuel consumption, and generate an assessable fuel consumption dataset based on each of the current assessable vehicle fuel consumption.

[0028] Furthermore, the integral evaluation generation module is also used for:

[0029] The process involves determining the actual proportion of each currently assessable vehicle fuel consumption level within its corresponding refined fuel consumption range; generating a current carbon credit assessment value for each currently assessable vehicle fuel consumption based on its actual proportion; obtaining the low-carbon management behavior of drivers and passengers in monitored vehicles, including data on paper ticket savings and in-vehicle air conditioning energy saving; generating a driver's low-carbon credit based on the paper ticket savings and in-vehicle air conditioning energy saving data; and generating a current vehicle trip carbon credit value based on the driver's low-carbon credit and each of the current carbon credit assessment values.

[0030] Furthermore, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps described in the above-described method for statistical analysis of carbon credit data based on corporate travel.

[0031] Furthermore, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps described in the above-described method for statistical analysis of carbon credit data based on corporate travel.

[0032] The technical effects achieved by this invention are as follows:

[0033] The aforementioned carbon credit data statistics method and system based on corporate travel involves the following steps: First, acquiring actual travel route data of vehicles under the supervision of the current carbon credit regulatory enterprise within a preset specific time period. Each vehicle under supervision has multiple actual travel route data points, each including actual travel distance and actual fuel consumption. Then, based on the actual travel distance and actual fuel consumption, the initial fuel consumption of the current vehicle is obtained. This initial fuel consumption is then subjected to fuel consumption quality inspection and screening, and an assessable fuel consumption dataset is generated after screening. This assessable fuel consumption dataset includes multiple currently assessable vehicle fuel consumption values. Next, carbon credit assessment is performed on the fuel consumption of each currently assessable vehicle, and a current carbon credit assessment estimate is generated for each. The low-carbon management behavior of the drivers and passengers of the vehicles under supervision is obtained, and a current vehicle trip carbon credit value is generated based on the low-carbon management behavior and each current carbon credit assessment estimate. Finally, the redeemable electricity data of each preset regulatory functional department within the current carbon credit regulatory enterprise is obtained, and a current electricity consumption value is generated based on the redeemable electricity data. The invention generates a summary carbon credit for the company's energy consumption based on the current vehicle trip carbon credit and the current electricity consumption carbon credit. Specifically, to enable targeted carbon credit statistics, the invention first sets different preset time periods and incorporates a positioning communication module and a fuel level detection module to avoid inaccurate fuel consumption statistics due to wear and tear on fuel consumption monitoring devices caused by different vehicle ages and consumption patterns. Furthermore, a fuel consumption quality inspection and screening step is implemented to screen the initial fuel consumption of the current vehicle, ensuring that the generated evaluable fuel consumption dataset is accurate and reasonable, thus enabling accurate subsequent carbon credit statistics. To further enhance the breadth of carbon credit statistics, the invention integrates electricity consumption data from electric vehicles that can be redeemed for carbon credits, thus combining travel data from both fuel-powered and new energy electric vehicles during company trips. This achieves accurate, efficient, and reliable acquisition of carbon credit data for company trips. Attached Figure Description

[0034] Figure 1 This is a flowchart illustrating a method for collecting carbon credit data based on corporate travel in one embodiment.

[0035] Figure 2 This is a block diagram of a carbon credit data statistics system based on corporate travel in one embodiment.

[0036] Figure 3 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0038] In one embodiment, such as Figure 1 As shown, this paper presents an application scenario for a carbon credit data statistics method based on corporate travel. This application scenario includes a smart terminal, a positioning and communication module, and a fuel consumption detection module. Both the positioning and communication module and the fuel consumption detection module are communicatively connected to the smart terminal. The positioning and communication module and the fuel consumption detection module are respectively used to detect and acquire the actual travel distance and actual fuel consumption of vehicles, and send these data to the smart terminal. This allows the smart terminal to obtain the actual travel route data of vehicles monitored by the current carbon credit monitoring enterprise within a preset specific time period. Each monitored vehicle has multiple actual travel route data points, and each actual travel route data point includes the actual travel distance and actual fuel consumption. Furthermore, based on the actual travel distance and actual fuel consumption... The process involves: acquiring the initial fuel consumption of current vehicles; performing fuel consumption quality inspection and screening on the initial fuel consumption; generating an evaluable fuel consumption dataset, which includes multiple evaluable fuel consumption values ​​for current vehicles; then evaluating the carbon credits of each evaluable fuel consumption and generating a current carbon credit estimate; acquiring the low-carbon management behavior of drivers and passengers in vehicles under actual travel supervision; and generating a current vehicle trip carbon credit value based on the low-carbon management behavior and the current carbon credit estimates; finally, acquiring the redeemable electricity consumption data of each preset regulatory department within the current carbon credit supervision enterprise; generating a current electricity consumption carbon credit value based on the redeemable electricity consumption data; and generating a current enterprise energy consumption summary carbon credit based on the current vehicle trip carbon credit value and the current electricity consumption carbon credit value.

[0039] The smart terminal may be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices.

[0040] In one embodiment, such as Figure 1 As shown, a method for statistical analysis of carbon credits based on corporate travel is provided, the method comprising:

[0041] Step S100: Obtain the actual travel route data of the actual travel monitoring vehicles of the current carbon credit monitoring enterprise within a preset specific time period. Each actual travel monitoring vehicle has multiple actual travel route data, and each actual travel route data includes the actual travel distance and the actual fuel consumption.

[0042] In this embodiment, the current carbon credit monitoring enterprise is the enterprise that needs to conduct carbon credit statistics, the actual travel monitoring vehicle is the vehicle owned by the current carbon credit monitoring enterprise, and the setting of the preset specific time period limits the time, so that carbon credit statistics can be carried out in a targeted manner. That is, by setting different preset specific time periods, carbon credit statistics for different time periods can be flexibly calculated.

[0043] Furthermore, when querying carbon credits later, the user can also preset a specific time period to perform detailed statistics and analysis on all carbon credits within that time period, thereby improving ease of use.

[0044] Unlike existing technologies, the actual travel distance and actual fuel consumption obtained in this invention are acquired by personnel who have pre-installed a positioning and communication module and a fuel level detection module on the vehicle. This differs from existing technologies that rely on the vehicle's own fuel consumption module. By incorporating these modules, this invention avoids the inaccuracy caused by wear and tear on fuel consumption statistics devices due to variations in vehicle age and wear conditions. It also overcomes the statistical errors resulting from different fuel consumption statistics methods used by different vehicles, thus achieving accurate acquisition of actual travel distance and actual fuel consumption.

[0045] Step S200: Based on the actual travel distance and actual fuel consumption, obtain the current vehicle initial fuel consumption, perform fuel consumption quality inspection and screening on the current vehicle initial fuel consumption, and generate an evaluable fuel consumption dataset after the screening is completed. The evaluable fuel consumption dataset includes multiple current evaluable vehicle fuel consumption data.

[0046] Step S300: Perform carbon credit assessment on the fuel consumption of each of the currently assessable vehicles, and generate current carbon credit assessment estimates respectively. Obtain the low-carbon management behavior of the drivers and passengers of the actual travel monitoring vehicles, and generate the current vehicle trip carbon credit value based on the low-carbon management behavior and each of the current carbon credit assessment estimates.

[0047] In this step, to ensure the accuracy and reliability of the acquired fuel consumption data, in addition to data acquisition based on the fuel consumption acquisition device, a fuel consumption quality inspection and screening step is further implemented. Specifically, the initial fuel consumption of the current vehicle is obtained based on the actual travel distance and actual fuel consumption. This initial fuel consumption is then subjected to fuel consumption quality inspection and screening, and an evaluable fuel consumption dataset is generated after screening. This evaluable fuel consumption dataset includes multiple current evaluable vehicle fuel consumption data. By setting the fuel consumption quality inspection and screening step, the obtained initial fuel consumption of the current vehicle is filtered to remove data with excessively low fuel consumption, thus preventing... To prevent companies from falsifying fuel consumption data, which could lead to falsification of carbon credit statistics and affect carbon credit statistics, this invention specifically aims to prevent automakers from modifying the installed positioning communication module and fuel level detection module, causing deviations in the obtained actual travel distance and actual fuel consumption that would result in values ​​lower than reasonable. It also aims to prevent automakers from altering the obtained actual travel distance and actual fuel consumption data, thereby causing inaccurate initial fuel consumption figures for the current vehicle. Therefore, this invention establishes a fuel consumption quality inspection and screening mechanism to ensure that the evaluable fuel consumption dataset generated after screening is accurate and reasonable, thus achieving accurate subsequent carbon credit statistics. Next, in order to generate carbon credits more accurately, carbon credit assessments are performed on the fuel consumption of each currently assessable vehicle, and a current carbon credit assessment estimate is generated for each. In addition, since drivers and passengers are an indispensable part of the vehicle's operation, their behavior should also be included in the vehicle's carbon credit statistics. Therefore, this invention also obtains the low-carbon management behavior of drivers and passengers in actual travel monitoring vehicles, and generates the current vehicle trip carbon credit value based on the low-carbon management behavior and the current carbon credit assessment estimates. Thus, carbon credit statistics are achieved by comprehensively considering vehicle-related trips and driver and passenger behavior, thereby improving the breadth of carbon credit statistics.

[0048] Step S400: Obtain the redeemable electricity consumption data of each preset regulatory functional department in the current carbon credit supervision enterprise, generate the current electricity consumption carbon credit value based on the redeemable electricity consumption data, and generate the current enterprise energy consumption summary carbon credit based on the current vehicle trip carbon credit value and the current electricity consumption carbon credit value.

[0049] Furthermore, to further enhance the breadth of carbon credit statistics, multiple pre-set regulatory departments are established, enabling separate management. On the other hand, by setting redeemable electricity consumption data as the current electricity consumption carbon credit value, the dimensionality of carbon credit statistics is increased, improving its reliability and accuracy. Specifically, redeemable electricity consumption data from each pre-set regulatory department within the current carbon credit regulatory enterprise is obtained. This redeemable electricity consumption data refers to the electricity consumed by electric vehicles that can be redeemed for carbon credits. Further, a current electricity consumption carbon credit value is generated based on this redeemable electricity consumption data. Then, the current vehicle trip carbon credit value and the current electricity consumption carbon credit value are added to generate the current enterprise's total energy consumption carbon credit. This integrates travel data from both fuel-powered vehicles and new energy electric vehicles during the enterprise's travel process, thereby achieving accurate, efficient, and reliable acquisition of carbon credit data for enterprise travel.

[0050] In one embodiment, step S200 involves: obtaining the initial fuel consumption of the current vehicle based on the actual travel distance and actual fuel consumption; performing a fuel consumption quality inspection and screening on the initial fuel consumption of the current vehicle; and generating an evaluable fuel consumption dataset after the screening is completed. The evaluable fuel consumption dataset includes multiple current evaluable vehicle fuel consumption data. Specifically, this includes:

[0051] Step S210: Obtain the initial fuel consumption of the current vehicle based on the actual travel distance and actual fuel consumption, and obtain the actual vehicle type of the actual travel monitoring vehicle;

[0052] Step S220: Obtain the standard rated fuel consumption corresponding to the actual vehicle type based on the actual vehicle type;

[0053] Step S230: Obtain the standard qualified fuel consumption range corresponding to the standard rated fuel consumption based on the standard rated fuel consumption, wherein the standard qualified fuel consumption range includes multiple refined fuel consumption ranges;

[0054] Step S240: Compare the current initial fuel consumption of the vehicle with the standard qualified fuel consumption range, and generate the current fuel consumption level of the current initial fuel consumption of the vehicle based on the comparison result. Each refined fuel consumption range corresponds to an actual standard fuel consumption level. When the current initial fuel consumption of the vehicle is within a refined fuel consumption range, the current fuel consumption level of the current initial fuel consumption of the vehicle is the actual standard fuel consumption level corresponding to the refined fuel consumption range.

[0055] Step S250: Filter the initial fuel consumption of the corresponding current vehicle according to the current fuel consumption level, filter out the current evaluable vehicle fuel consumption, and generate an evaluable fuel consumption dataset based on each of the current evaluable vehicle fuel consumption.

[0056] Furthermore, to ensure the accuracy and reliability of carbon credit statistics, the initial fuel consumption of the current vehicle is first obtained based on the actual travel distance and actual fuel consumption, and the actual vehicle type of the monitored vehicle is also obtained. Then, the standard rated fuel consumption corresponding to the actual vehicle type is obtained. Next, the standard qualified fuel consumption range corresponding to the standard rated fuel consumption is obtained, and the standard qualified fuel consumption range includes multiple refined fuel consumption ranges. Then, the initial fuel consumption of the current vehicle is compared with the standard qualified fuel consumption ranges, and the current fuel consumption level of the initial fuel consumption of the current vehicle is generated based on the comparison results. Each refined fuel consumption range corresponds to a specific fuel consumption level. A standard fuel consumption level is defined as follows: when the initial fuel consumption of the current vehicle falls within a refined fuel consumption range, the current fuel consumption level of the initial fuel consumption of the current vehicle is the standard fuel consumption level corresponding to the refined fuel consumption range. Finally, the initial fuel consumption of the current vehicle is filtered according to the current fuel consumption level, and the currently evaluable vehicle fuel consumption is selected. An evaluable fuel consumption dataset is then generated based on each of the currently evaluable vehicle fuel consumptions. Specifically, in this embodiment, the specific calculation method for the initial fuel consumption of the current vehicle is: fuel consumed ÷ mileage × 100 = fuel consumption per 100 kilometers. The initial fuel consumption of the current vehicle is the actual fuel consumption. Therefore, to determine whether the calculated fuel consumption is reasonable, it is compared with the original vehicle calibration fuel consumption. In comparison, specifically, the standard rated fuel consumption is obtained. After obtaining the standard rated fuel consumption, a standard acceptable fuel consumption range matching the standard rated fuel consumption is further obtained. The standard acceptable fuel consumption range is statistically determined by those skilled in the art and represents the reasonable fuel consumption range for this vehicle model. The standard acceptable fuel consumption range is divided by the standard rated fuel consumption. In this embodiment, the standard acceptable fuel consumption range includes four refined fuel consumption ranges, with the standard rated fuel consumption as the central dividing line. Specifically, the standard rated fuel consumption is represented by E, the minimum value in the standard acceptable fuel consumption range is represented by A, and the maximum value in the standard acceptable fuel consumption range is represented by B. A reasonable low fuel consumption C is set between A and E, and B... A reasonable high fuel consumption range D is set between E and B. Specifically, the range between A and C is the first fuel consumption range, the range between C and E is the second fuel consumption range, the range between E and D is the third fuel consumption range, and the range between D and B is the fourth fuel consumption range. That is, the refined fuel consumption range includes multiple ranges. In this embodiment, the first fuel consumption range, the second fuel consumption range, the third fuel consumption range, and the fourth fuel consumption range are all the refined fuel consumption ranges. The first fuel consumption range, the second fuel consumption range, the third fuel consumption range, and the fourth fuel consumption range all correspond to the actual standard fuel consumption level. Specifically, the actual standard fuel consumption levels corresponding to the first fuel consumption range, the second fuel consumption range, the third fuel consumption range, and the fourth fuel consumption range are the first level, the second level, the third level, and the fourth level, respectively.

[0057] Furthermore, when comparing the current initial fuel consumption of the vehicle with the standard acceptable fuel consumption range, it is determined which refined fuel consumption range the current initial fuel consumption belongs to, and the current fuel consumption level of the current initial fuel consumption is generated.

[0058] Next, when filtering the initial fuel consumption of the current vehicle according to the current fuel consumption level, if it is determined that the initial fuel consumption of the current vehicle does not belong to any of the first, second, third, and fourth levels, it is determined that a fuel consumption statistics fault has occurred. Further, it is determined whether the initial fuel consumption of the current vehicle is less than the minimum value A. If it is, the corresponding initial fuel consumption of the current vehicle is filtered out and not included in the statistics, and a data check instruction is generated. The data check instruction is used to send an inspection instruction to the staff, so as to facilitate the staff to perform data statistics to check whether there is a data manipulation problem, thereby avoiding carbon credit statistics fraud.

[0059] Next, it is determined whether the current initial fuel consumption of the vehicle is greater than the maximum value B. If it is determined to be greater than the maximum value B, then the number of times the current initial fuel consumption of the vehicle is greater than the maximum value B is counted. It is further determined whether the number of times it is greater than the maximum value B is greater than a preset number. If it is, then the average fuel consumption of the current initial fuel consumption of the vehicle for a specific number of times is set to the maximum value B, so as to update the maximum value B.

[0060] Therefore, by setting the standard acceptable fuel consumption range and the refined fuel consumption range, and comparing the current vehicle's initial fuel consumption with the standard acceptable fuel consumption range, the current vehicle's initial fuel consumption can be filtered out. Thus, the current assessable vehicle fuel consumption can be filtered out, and an assessable fuel consumption dataset can be generated based on each of the current assessable vehicle fuel consumptions, so as to achieve accurate and reliable carbon credit statistics in the future.

[0061] In one embodiment, step S300: Carbon credit assessment is performed on the fuel consumption of each of the currently assessable vehicles, and a current carbon credit assessment estimate is generated for each; the low-carbon management behavior of the drivers and passengers of the vehicles under actual travel supervision is obtained; and a current vehicle trip carbon credit value is generated based on the low-carbon management behavior and each of the current carbon credit assessment estimates; specifically including:

[0062] Step S310: Determine the actual proportion of the current fuel consumption level corresponding to each of the currently assessable vehicle fuel consumption levels in the corresponding refined fuel consumption range;

[0063] Step S320: Generate the current carbon credit assessment value corresponding to the current assessable vehicle fuel consumption according to each of the actual occupancy ratios;

[0064] Step S330: Obtain the low-carbon management behavior of drivers and passengers in the actual travel monitoring vehicles, wherein the low-carbon management behavior includes data on paper ticket savings and data on energy saving of in-vehicle air conditioning;

[0065] Step S340: Generate driver low-carbon points based on the paper ticket saving data and the vehicle air conditioning energy saving data;

[0066] Step S350: Generate the current vehicle trip carbon credit value based on the driver's low carbon credit and the current carbon credit assessment estimates.

[0067] Furthermore, in this embodiment, to achieve a more accurate carbon credit valuation, the actual proportion of each current fuel consumption level corresponding to the current assessable vehicle fuel consumption within the corresponding refined fuel consumption range is first determined. Then, a current carbon credit valuation is generated based on each actual proportion. Next, the low-carbon management behavior of the drivers and passengers of the monitored vehicles is obtained, including paper ticket savings data and in-vehicle air conditioning energy-saving data. Then, a driver's low-carbon credit is generated based on the paper ticket savings data and the in-vehicle air conditioning energy-saving data. Finally, the current vehicle trip carbon credit value is generated based on the driver's low-carbon credit and each current carbon credit valuation. That is, in this invention, the actual proportion is used for evaluation, and the actual proportion is the value used to evaluate the carbon credit. Specifically, the carbon credit valuation corresponding to the current assessable vehicle fuel consumption that is greater than the standard rated fuel consumption is negative, and vice versa. The carbon credit estimate corresponding to the current assessable vehicle fuel consumption is positive. Therefore, the carbon credit estimate corresponding to the current assessable vehicle fuel consumption in the first and second fuel consumption ranges is positive, while the carbon credit estimate corresponding to the current assessable vehicle fuel consumption in the third and fourth fuel consumption ranges is negative. Taking the second fuel consumption range as an example, if the total fuel consumption in the second fuel consumption range is 1, then the total fuel consumption difference between C and D is 1. When the specific value of the current assessable vehicle fuel consumption is greater than C by 0.2, that is, when the current assessable vehicle fuel consumption is C+0.2, the difference between the current assessable vehicle fuel consumption and E is 0.8. The further away from E, the higher the carbon credit should be. Therefore, the actual proportion is 80%. Taking the standard carbon credit corresponding to E as 0, and the score for being 10% away from E as an example, then the current assessable vehicle fuel consumption is C+0.2, that is, the current carbon credit estimate with an actual proportion of 80% is +80 points, thus realizing the generation of the current carbon credit estimate.

[0068] Next, if drivers and passengers engage in relevant low-carbon behaviors, these should be statistically analyzed and managed. Specifically, this includes data on paper ticket savings and in-vehicle air conditioning energy savings. The paper ticket savings data refers to the specific receipt of electronic invoices, and the in-vehicle air conditioning energy saving data includes energy-saving data. Then, based on the paper ticket savings data and the in-vehicle air conditioning energy saving data, a driver's low-carbon credit is generated. Finally, the driver's low-carbon credit and the current carbon credit assessment estimate are combined to generate the current vehicle trip carbon credit value, thus ensuring the accuracy and reliability of the current vehicle trip carbon credit value.

[0069] In one embodiment, step S100: Obtain actual travel route data of the vehicles monitored by the current carbon credit regulatory enterprise within a preset specific time period. Each monitored vehicle has multiple actual travel route data, and each actual travel route data includes the actual travel distance and the actual fuel consumption, specifically including:

[0070] Step S110: Obtain a successful positioning installation instruction for the positioning communication module installed on the actual travel monitoring vehicle;

[0071] Step S120: Generate a location activation command based on the location installation success command, and send the location activation command to the location communication module. The location activation command is used to activate the location communication module.

[0072] Step S130: Obtain a successful fuel level detection instruction from the fuel level detection module installed on the actual travel monitoring vehicle;

[0073] Step S140: Generate a fuel level detection start command based on the fuel level detection success command, and send the fuel level detection start command to the fuel level detection module. The fuel level detection start command is used to start the fuel level detection module.

[0074] Step S150: Obtain the actual travel distance based on the positioning communication module, and obtain the actual fuel consumption based on the fuel level detection module, wherein the combination of the actual travel distance and the actual fuel consumption is the actual travel route data.

[0075] Furthermore, in this embodiment, to accurately obtain the actual travel distance and actual fuel consumption, a successful positioning installation command is obtained from the positioning communication module installed on the actual travel monitoring vehicle; a positioning activation command is generated based on the successful positioning installation command and sent to the positioning communication module, which is used to activate the positioning communication module; a successful fuel level detection command is obtained from the fuel level detection module installed on the actual travel monitoring vehicle; a fuel level detection activation command is generated based on the successful fuel level detection command and sent to the fuel level detection module, which is used to activate the fuel level detection module. That is, this invention achieves accurate information acquisition by setting up its own data acquisition device instead of relying on the vehicle's original fuel consumption statistics device. Furthermore, the actual travel distance is obtained based on the positioning communication module, and the actual fuel consumption is obtained based on the fuel level detection module. Thus, the combination of the actual travel distance and the actual fuel consumption constitutes the actual travel route data, providing a precise data foundation for subsequent accurate carbon credit statistics.

[0076] In one embodiment, step S400: obtaining the redeemable electricity consumption data of each preset regulatory functional department in the current carbon credit supervision enterprise, generating the current electricity consumption carbon credit value based on the redeemable electricity consumption data, and generating the current enterprise's total energy consumption carbon credit based on the current vehicle trip carbon credit value and the current electricity consumption carbon credit value, specifically including:

[0077] Step S410: Obtain the rated statistical electricity consumption of each preset regulatory functional department in the current carbon credit regulatory enterprise;

[0078] Step S420: Calculate the initial actual electricity consumption of each preset regulatory functional department in the current carbon credit regulatory enterprise;

[0079] Step S430: Generate the actual power saving based on the rated statistical power consumption and the initial actual power consumption;

[0080] Step S440: Obtain the peak power consumption during peak hours based on the initial actual power consumption;

[0081] Step S450: Generate peak power relocation amount based on the difference between the peak power consumption and the preset standard power consumption;

[0082] Step S460: Generate redeemable electricity consumption data based on the actual electricity saved and the peak electricity consumption, and generate the current electricity carbon credit value based on the redeemable electricity consumption data;

[0083] Step S470: Generate the current enterprise energy consumption summary carbon credits based on the current vehicle trip carbon credit value and the current electricity consumption carbon credit value.

[0084] Furthermore, in this embodiment, in order to comprehensively calculate carbon credits based on the electricity consumption of new energy vehicles, the rated statistical electricity consumption of each preset regulatory functional department in the current carbon credit supervision enterprise is obtained; then, the initial actual electricity consumption of each preset regulatory functional department in the current carbon credit supervision enterprise is calculated; next, actual energy savings are generated based on the rated statistical electricity consumption and the initial actual electricity consumption; peak electricity consumption during peak hours is obtained based on the initial actual electricity consumption; then, peak allowable electricity consumption is generated based on the difference between the peak electricity consumption and the preset standard electricity consumption; finally, exchangeable electricity data is generated based on the actual energy savings and the peak allowable electricity consumption, and the current electricity carbon credit value is generated based on the exchangeable electricity data; and the current enterprise energy consumption summary carbon credit is generated based on the current vehicle trip carbon credit value and the current electricity carbon credit value. Specifically, in this embodiment, the rated statistical electricity consumption of each preset regulatory functional department in the current carbon credit supervision enterprise is obtained; then, the initial actual electricity consumption of each preset regulatory functional department in the current carbon credit supervision enterprise is calculated; next, actual energy savings are generated based on the rated statistical electricity consumption and the initial actual electricity consumption; peak electricity consumption of each preset regulatory functional department in the current carbon credit supervision enterprise is obtained ... The electricity consumption is the theoretically required rated electricity consumption of new energy vehicles held by the pre-defined regulatory departments. When the consumption is the rated statistical electricity, it meets the carbon emission standards, so the carbon credit should be 0. However, if it falls below this standard, carbon credits can be exchanged. Therefore, the initial actual electricity consumption is statistically analyzed, and the actual electricity savings are further statistically analyzed to achieve the electricity savings. In addition, the electricity savings during peak electricity consumption periods can also be exchanged for carbon credits. This invention generates peak electricity allowances to obtain the electricity allowance contributions made during peak electricity consumption periods. It then integrates the total electricity consumption and the electricity consumption time period to generate exchangeable electricity consumption data. Based on the exchangeable electricity consumption data, the current electricity carbon credit value is generated. The current vehicle trip carbon credit value and the current electricity carbon credit value are then added together to generate the current enterprise energy consumption summary carbon credit. Therefore, the carbon credit statistics are comprehensive and reliable.

[0085] In summary, the carbon credit data statistics method and system based on corporate travel of the present invention sequentially obtains the actual travel route data of the actual travel monitored vehicles of the current carbon credit monitoring enterprise within a preset specific time period. Each actual travel monitored vehicle has multiple actual travel route data, and each actual travel route data includes the actual travel distance and the actual fuel consumption. Then, based on the actual travel distance and actual fuel consumption, the initial fuel consumption of the current vehicle is obtained. The initial fuel consumption of the current vehicle is then subjected to fuel consumption quality inspection and screening, and an assessable fuel consumption dataset is generated after screening. The assessable fuel consumption dataset includes the fuel consumption of multiple currently assessable vehicles. Next, carbon credit assessment is performed on the fuel consumption of each currently assessable vehicle, and a current carbon credit assessment estimate is generated for each. The low-carbon management behavior of the drivers and passengers of the actual travel monitored vehicles is obtained, and the current vehicle trip carbon credit value is generated based on the low-carbon management behavior and each current carbon credit assessment estimate. Finally, the redeemable electricity data of each preset regulatory functional department in the current carbon credit monitoring enterprise is obtained, and the redeemable electricity data is used to generate... The invention generates a current carbon credit value based on the current vehicle trip carbon credit value and the current electricity consumption carbon credit value. Specifically, to enable targeted carbon credit statistics, the invention first sets different preset specific time periods and incorporates a positioning communication module and a fuel level detection module to collect information. This avoids inaccurate fuel consumption statistics due to wear and tear of fuel consumption statistical devices caused by different vehicle ages and consumption conditions. Furthermore, a fuel consumption quality inspection and screening step is implemented to screen the initial fuel consumption of the current vehicle, ensuring that the generated evaluable fuel consumption dataset is accurate and reasonable, thus achieving accurate subsequent carbon credit statistics. To further enhance the breadth of carbon credit statistics, the invention specifically integrates electricity consumption data from electric vehicles that can be redeemed for carbon credits, thus combining travel data from both fuel-powered vehicles and new energy electric vehicles during corporate trips. This achieves accurate, efficient, and reliable acquisition of corporate travel carbon credit data statistics.

[0086] In one embodiment, the carbon credit data statistical method based on corporate travel further includes the following steps:

[0087] Step S510: Obtain the basic carbon credit account of the carbon credit authorized employee who has authorized carbon credit transfer in the current carbon credit supervision enterprise, wherein the basic carbon credit account belongs to the current carbon credit supervision enterprise and is associated with the enterprise carbon credit account of the current carbon credit supervision enterprise.

[0088] Step S520: Real-time acquisition of newly added travel carbon credits in the basic carbon credit account of the carbon credit authorized employee within the preset specific time period;

[0089] Step S530: Based on the newly added travel carbon credits, filter out the carbon credits for official travel from the newly added travel carbon credits;

[0090] Step S540: Obtain a pre-set specific carbon credit extraction ratio, filter the official travel carbon credits based on the specific carbon credit extraction ratio, obtain available employee carbon credits, and import the available employee carbon credits into the enterprise carbon credit account.

[0091] In this embodiment, the import of employees' official carbon credits into the enterprise carbon credit account is proposed for the first time. Specifically, the authorization of the employees is first obtained, that is, the basic carbon credit account of the employees who have authorized the transfer of carbon credits in the current carbon credit supervision enterprise is obtained. The basic carbon credit account belongs to the current carbon credit supervision enterprise and is associated with the enterprise carbon credit account of the current carbon credit supervision enterprise. In other words, it can be understood that the enterprise carbon credit account can interact with the basic carbon credit account.

[0092] Furthermore, to achieve more accurate carbon credit interaction, a specific carbon credit extraction ratio is set while ensuring the carbon credit benefits already possessed by employees. This specific extraction ratio is less than 100%, ensuring that the available employee carbon credits obtained after filtering official travel carbon credits based on this ratio do not represent all of the employee's carbon credits, but rather a portion. These available employee carbon credits are then used to compensate employees at market prices. This enhances employees' low-carbon incentives during official travel and broadens the scope of carbon credit statistics for corporate travel, achieving more comprehensive and compatible carbon credit statistics.

[0093] In one embodiment, such as Figure 2 As shown, a carbon credit data statistics system based on corporate travel is provided, the system comprising:

[0094] The actual route acquisition module is used to acquire the actual travel route data of the actual travel monitoring vehicles of the current carbon credit monitoring enterprise within a preset specific time period. Each actual travel monitoring vehicle has multiple actual travel route data, and each actual travel route data includes the actual travel distance and the actual fuel consumption.

[0095] The fuel consumption quality inspection and screening module is used to obtain the current vehicle's initial fuel consumption based on the actual travel distance and actual fuel consumption, perform fuel consumption quality inspection and screening on the current vehicle's initial fuel consumption, and generate an evaluable fuel consumption dataset after the screening is completed. The evaluable fuel consumption dataset includes multiple current evaluable vehicle fuel consumption data.

[0096] The carbon credit assessment generation module is used to assess the fuel consumption of each of the currently assessable vehicles and generate a current carbon credit assessment estimate, obtain the low-carbon management behavior of the drivers and passengers of the actual travel monitored vehicles, and generate the current vehicle trip carbon credit value based on the low-carbon management behavior and each of the current carbon credit assessment estimates.

[0097] The summary points generation module is used to obtain the redeemable electricity consumption data of each preset regulatory functional department in the current carbon points supervision enterprise, generate the current electricity consumption carbon points value based on the redeemable electricity consumption data, and generate the current enterprise's energy consumption summary carbon points based on the current vehicle trip carbon points value and the current electricity consumption carbon points value.

[0098] In one embodiment, the fuel consumption quality inspection and screening module further includes:

[0099] The vehicle type acquisition module is used to obtain the initial fuel consumption of the current vehicle based on the actual travel distance and actual fuel consumption, and to obtain the actual vehicle type of the actual travel monitored vehicle.

[0100] A rated fuel consumption acquisition module is used to acquire a standard rated fuel consumption corresponding to the actual vehicle type based on the actual vehicle type.

[0101] The fuel consumption range refinement module is used to obtain the standard qualified fuel consumption range corresponding to the standard rated fuel consumption based on the standard rated fuel consumption. The standard qualified fuel consumption range includes multiple refined fuel consumption ranges.

[0102] The fuel consumption range comparison module is used to compare the current initial fuel consumption of the vehicle with the standard qualified fuel consumption range, and generate the current fuel consumption level of the current initial fuel consumption based on the comparison result. Each refined fuel consumption range corresponds to an actual standard fuel consumption level. When the current initial fuel consumption of the vehicle is within a refined fuel consumption range, the current fuel consumption level of the current initial fuel consumption is the actual standard fuel consumption level corresponding to the refined fuel consumption range.

[0103] The fuel consumption assessment generation module is used to filter the initial fuel consumption of the corresponding current vehicle according to the current fuel consumption level, filter out the current assessable vehicle fuel consumption, and generate an assessable fuel consumption dataset based on each of the current assessable vehicle fuel consumption.

[0104] In one embodiment, the integral evaluation generation module is further configured to:

[0105] The process involves determining the actual proportion of each currently assessable vehicle fuel consumption level within its corresponding refined fuel consumption range; generating a current carbon credit assessment value for each currently assessable vehicle fuel consumption based on its actual proportion; obtaining the low-carbon management behavior of drivers and passengers in monitored vehicles, including data on paper ticket savings and in-vehicle air conditioning energy saving; generating a driver's low-carbon credit based on the paper ticket savings and in-vehicle air conditioning energy saving data; and generating a current vehicle trip carbon credit value based on the driver's low-carbon credit and each of the current carbon credit assessment values.

[0106] In one embodiment, the actual route acquisition module is further configured to: acquire a successful location installation instruction for a location communication module installed on the actual travel monitoring vehicle; generate a location activation instruction based on the successful location installation instruction, and send the location activation instruction to the location communication module, wherein the location activation instruction is used to activate the location communication module; acquire a successful fuel level detection instruction for a fuel level detection module installed on the actual travel monitoring vehicle; generate a fuel level detection activation instruction based on the successful fuel level detection instruction, and send the fuel level detection activation instruction to the fuel level detection module, wherein the fuel level detection activation instruction is used to activate the fuel level detection module; acquire the actual travel distance based on the location communication module, and acquire the actual fuel consumption based on the fuel level detection module, wherein the combination of the actual travel distance and the actual fuel consumption constitutes the actual travel route data.

[0107] In one embodiment, the aggregated points generation module is further configured to: obtain the rated statistical electricity consumption of each preset regulatory functional department in the current carbon credit supervision enterprise; calculate the initial actual electricity consumption of each preset regulatory functional department in the current carbon credit supervision enterprise; generate actual energy savings based on the rated statistical electricity consumption and the initial actual electricity consumption; obtain the peak electricity consumption during peak periods based on the initial actual electricity consumption; generate peak allowable electricity consumption based on the difference between the peak electricity consumption and the preset standard electricity consumption; generate redeemable electricity data based on the actual energy savings and the peak allowable electricity consumption, and generate the current electricity carbon credit value based on the redeemable electricity data; and generate the current enterprise energy consumption aggregated carbon credit based on the current vehicle trip carbon credit value and the current electricity carbon credit value.

[0108] In another embodiment of the present invention, the carbon credit data statistics system based on corporate travel further includes a carbon credit interaction module, which is used to: obtain the basic carbon credit accounts of carbon credit authorized employees who have authorized carbon credit transfer in the current carbon credit supervision enterprise, wherein the basic carbon credit accounts belong to the current carbon credit supervision enterprise and are associated with the enterprise carbon credit account of the current carbon credit supervision enterprise; obtain in real time the newly added travel carbon credits in the basic carbon credit accounts of the carbon credit authorized employees within the preset specific time period; filter out official travel carbon credits from the newly added travel carbon credits based on the newly added travel carbon credits; obtain a preset specific carbon credit extraction ratio, filter the official travel carbon credits based on the specific carbon credit extraction ratio, obtain available employee carbon credits, and import the available employee carbon credits into the enterprise carbon credit account.

[0109] In one embodiment, such as Figure 3 As shown, a computer device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps described in the above-described method for statistical analysis of carbon credit data based on corporate travel.

[0110] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps described in the above-described method for statistical analysis of carbon credits based on corporate travel data.

[0111] 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, and when executed, it 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 can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various 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), etc.

[0112] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0113] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for statistical analysis of carbon credits based on corporate travel, characterized in that, The method includes: The system obtains the actual travel route data of the vehicles monitored by the current carbon credit regulatory enterprise within a preset specific time period. Each vehicle monitored has multiple actual travel route data, and each actual travel route data includes the actual travel distance and the actual fuel consumption. The actual travel distance and actual fuel consumption are obtained through a positioning and communication module and a fuel level detection module that are additionally installed on the vehicle itself. Based on the actual travel distance and actual fuel consumption, the initial fuel consumption of the current vehicle is obtained. The initial fuel consumption of the current vehicle is then subjected to fuel consumption quality inspection and screening. After the screening is completed, an evaluable fuel consumption dataset is generated, which includes multiple current evaluable vehicle fuel consumption data. Carbon credit assessment is performed on the fuel consumption of each of the currently assessable vehicles, and a current carbon credit assessment estimate is generated for each. The low-carbon management behavior of the drivers and passengers of the actual travel monitored vehicles is obtained, and the current vehicle trip carbon credit value is generated based on the low-carbon management behavior and the current carbon credit assessment estimate. The low-carbon management behavior includes paper ticket saving data and in-vehicle air conditioning energy saving data. Obtain the redeemable electricity consumption data of each preset regulatory functional department in the current carbon credit supervision enterprise, generate the current electricity consumption carbon credit value based on the redeemable electricity consumption data, and generate the current enterprise energy consumption summary carbon credit based on the current vehicle trip carbon credit value and the current electricity consumption carbon credit value. The steps to generate an assessable fuel consumption dataset include: The initial fuel consumption of the current vehicle is obtained based on the actual travel distance and actual fuel consumption, and the actual vehicle type of the monitored vehicle is also obtained. Obtain the standard rated fuel consumption corresponding to the actual vehicle type; Obtain the standard acceptable fuel consumption range corresponding to the standard rated fuel consumption, wherein the standard acceptable range includes multiple refined fuel consumption ranges; The current initial fuel consumption of the vehicle is compared with the standard acceptable fuel consumption range, and the current fuel consumption level of the current initial fuel consumption is generated based on the comparison result. Each of the refined fuel consumption ranges has a corresponding actual standard fuel consumption level. When the current initial fuel consumption of the vehicle is within a refined fuel consumption range, the current fuel consumption level of the current initial fuel consumption is the actual standard fuel consumption level corresponding to the refined fuel consumption range. The initial fuel consumption of the corresponding vehicle is filtered according to the current fuel consumption level, and the current evaluable fuel consumption of the vehicle is selected. An evaluable fuel consumption dataset is generated based on the current evaluable fuel consumption of each vehicle. A reasonable low fuel consumption C is set between the minimum value A in the standard acceptable fuel consumption range and the standard rated fuel consumption E; a reasonable high fuel consumption D is set between the maximum value B in the standard acceptable fuel consumption range and E; the range between A and C is the first fuel consumption range, the range between C and E is the second fuel consumption range, the range between E and D is the third fuel consumption range, and the range between D and B is the fourth fuel consumption range; the first to fourth fuel consumption ranges correspond to the first to fourth levels of fuel consumption, respectively. If the current initial fuel consumption of the vehicle does not belong to any of the first to fourth levels, a fuel consumption statistics fault is determined to have occurred; if the current initial fuel consumption of the vehicle is less than the minimum value A, the corresponding current initial fuel consumption of the vehicle is filtered out, and a data check instruction is generated to avoid carbon credit statistics fraud; if the current initial fuel consumption of the vehicle is greater than the maximum value B, the number of times it is greater than the maximum value B is counted, and if the number of counts is greater than the preset number, the maximum value B is updated. The method further includes: Obtain the basic carbon credit accounts of the authorized employees of the current carbon credit supervision enterprise who have authorized carbon credit transfer, wherein the basic carbon credit accounts belong to the current carbon credit supervision enterprise and are associated with the enterprise carbon credit accounts of the current carbon credit supervision enterprise. Real-time acquisition of new travel carbon credits in the basic carbon credit accounts of the carbon credit authorized employees within the preset specific time period. Official travel carbon credits are selected from the newly added travel carbon credits. Obtain a pre-set specific carbon credit extraction ratio, filter the carbon credits for official travel based on the specific carbon credit extraction ratio, obtain available employee carbon credits, and import the available employee carbon credits into the enterprise carbon credit account.

2. The carbon credit data statistical method based on corporate travel according to claim 1, characterized in that, Carbon credit assessments are performed on the fuel consumption of each currently assessable vehicle, and current carbon credit assessment estimates are generated for each. The low-carbon management behavior of drivers and passengers in the monitored vehicles is obtained, and a current vehicle trip carbon credit value is generated based on the low-carbon management behavior and the current carbon credit assessment estimates. Specifically, this includes: Determine the actual proportion of each of the current fuel consumption levels corresponding to the current assessable vehicle fuel consumption within the corresponding refined fuel consumption range; generate the current carbon credit assessment value corresponding to the current assessable vehicle fuel consumption based on each actual proportion; obtain the low-carbon management behavior of drivers and passengers of the actual travel monitored vehicles, and generate driver low-carbon credits based on the paper ticket saving data and the in-vehicle air conditioning energy saving data; generate the current vehicle trip carbon credit value based on the driver low-carbon credits and each of the current carbon credit assessment values.

3. The carbon credit data statistical method based on corporate travel according to claim 1, characterized in that, The system acquires actual travel route data of vehicles monitored by current carbon credit regulatory enterprises within a preset specific time period. Each monitored vehicle has multiple actual travel route data points, and each actual travel route data point includes the actual travel distance and actual fuel consumption, specifically including: The system acquires a successful installation command for a positioning communication module installed on the actual travel monitoring vehicle; generates a positioning activation command based on the successful installation command and sends the activation command to the positioning communication module, which is used to activate the positioning communication module; acquires a successful fuel level detection command for a fuel level detection module installed on the actual travel monitoring vehicle; generates a fuel level detection activation command based on the successful fuel level detection command and sends the activation command to the fuel level detection module, which is used to activate the fuel level detection module; acquires the actual travel distance based on the positioning communication module and the actual fuel consumption based on the fuel level detection module, wherein the combination of the actual travel distance and the actual fuel consumption constitutes the actual travel route data.

4. The method for statistical analysis of carbon credits based on corporate travel according to claim 1, characterized in that, Obtain the redeemable electricity consumption data of each preset regulatory functional department in the current carbon credit supervision enterprise, generate the current electricity consumption carbon credit value based on the redeemable electricity consumption data, and generate the current enterprise's total energy consumption carbon credit based on the current vehicle trip carbon credit value and the current electricity consumption carbon credit value, specifically including: The system obtains the rated statistical electricity consumption of each preset regulatory functional department within the current carbon credit monitoring enterprise; calculates the initial actual electricity consumption of each preset regulatory functional department within the current carbon credit monitoring enterprise; generates actual electricity savings based on the rated statistical electricity consumption and the initial actual electricity consumption; obtains peak electricity consumption during peak hours based on the initial actual electricity consumption; generates peak allowable electricity consumption based on the difference between the peak electricity consumption and the preset standard electricity consumption; generates redeemable electricity data based on the actual electricity savings and the peak allowable electricity consumption, and generates the current electricity carbon credit value based on the redeemable electricity data; and generates the current enterprise's total energy consumption carbon credit based on the current vehicle trip carbon credit value and the current electricity carbon credit value.

5. A carbon credit data statistics system based on corporate travel, characterized in that, The system applies the carbon credit data statistical method based on corporate travel as described in any one of claims 1-4, and the system comprises: The actual route acquisition module is used to acquire the actual travel route data of the actual travel monitoring vehicles of the current carbon credit monitoring enterprise within a preset specific time period. Each actual travel monitoring vehicle has multiple actual travel route data, and each actual travel route data includes the actual travel distance and the actual fuel consumption. The actual travel distance and the actual fuel consumption are acquired by a positioning communication module and a fuel level detection module that are additionally installed on the vehicle itself. The fuel consumption quality inspection and screening module is used to obtain the current vehicle's initial fuel consumption based on the actual travel distance and actual fuel consumption, perform fuel consumption quality inspection and screening on the current vehicle's initial fuel consumption, and generate an evaluable fuel consumption dataset after the screening is completed. The evaluable fuel consumption dataset includes multiple current evaluable vehicle fuel consumption data. The carbon credit assessment generation module is used to assess the carbon credits of the fuel consumption of each of the currently assessable vehicles, generate the current carbon credit assessment value, obtain the low-carbon management behavior of the drivers and passengers of the actual travel monitored vehicles, and generate the current vehicle trip carbon credit value based on the low-carbon management behavior and the current carbon credit assessment value. The low-carbon management behavior includes paper ticket saving data and in-vehicle air conditioning energy saving data. The summary points generation module is used to obtain the redeemable electricity consumption data of each preset regulatory functional department in the current carbon points supervision enterprise, generate the current electricity consumption carbon points value based on the redeemable electricity consumption data, and generate the current enterprise's energy consumption summary carbon points based on the current vehicle trip carbon points value and the current electricity consumption carbon points value.

6. The carbon credit data statistics system based on corporate travel according to claim 5, characterized in that, The fuel consumption quality inspection and screening module also includes: The vehicle type acquisition module is used to obtain the initial fuel consumption of the current vehicle based on the actual travel distance and actual fuel consumption, and to obtain the actual vehicle type of the actual travel monitored vehicle. A rated fuel consumption acquisition module is used to acquire a standard rated fuel consumption corresponding to the actual vehicle type based on the actual vehicle type. The fuel consumption range refinement module is used to obtain the standard qualified fuel consumption range corresponding to the standard rated fuel consumption based on the standard rated fuel consumption. The standard qualified fuel consumption range includes multiple refined fuel consumption ranges. The fuel consumption range comparison module is used to compare the current initial fuel consumption of the vehicle with the standard qualified fuel consumption range, and generate the current fuel consumption level of the current initial fuel consumption based on the comparison result. Each refined fuel consumption range corresponds to an actual standard fuel consumption level. When the current initial fuel consumption of the vehicle is within a refined fuel consumption range, the current fuel consumption level of the current initial fuel consumption is the actual standard fuel consumption level corresponding to the refined fuel consumption range. The fuel consumption assessment generation module is used to filter the initial fuel consumption of the corresponding current vehicle according to the current fuel consumption level, filter out the current assessable vehicle fuel consumption, and generate an assessable fuel consumption dataset based on each of the current assessable vehicle fuel consumption.

7. The carbon credit data statistics system based on corporate travel according to claim 6, characterized in that, The integral evaluation generation module is also used for: Determine the actual proportion of the current fuel consumption level corresponding to each of the currently assessable vehicle fuel consumption levels within the corresponding refined fuel consumption range; generate the current carbon credit assessment value corresponding to the current assessable vehicle fuel consumption based on each actual proportion. To obtain information on the low-carbon management practices of drivers and passengers in vehicles subject to actual travel monitoring; Driver low-carbon points are generated based on the paper ticket savings data and the vehicle air conditioning energy-saving data; The current vehicle trip carbon credit value is generated based on the driver's low-carbon credits and the current carbon credit assessment estimates.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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