A Compensation Method, Device, Equipment and Storage Medium for Passenger Flow Data

Abstract

An embodiment of the present application provides a method, device, equipment, and storage medium for compensating passenger flow data, relating to the technical field of data processing, to solve the problem of loss of passenger flow data in the prior art. Specifically, it includes: determining that there is a lack of passenger flow data in the passenger flow equipment group within the first time interval; the passenger flow equipment group includes one or more devices for collecting passenger flow data; determining the time attribute of the first time interval, where the time attribute includes the time category and the time length; according to the time attribute of the first time interval, obtaining the passenger flow data of the passenger flow equipment group within the second time interval, the second time interval has the same time attribute as the first time interval, and the time interval between the second time interval and the first time interval is less than the first preset threshold; predicting at least the missing passenger flow data of the passenger flow equipment group within the first time interval based on the passenger flow data of the passenger flow equipment group within the second time interval.

Description

Technical Field

[0001] This application relates to the field of data processing, and specifically provides a method, device, equipment, and storage medium for compensating passenger flow data. Background Art

[0002] Currently, during the operation of a passenger flow system, it is inevitable that passenger flow data is lost due to various reasons, such as damage to passenger flow equipment, network problems, platform defects, etc. When customers need to use passenger flow data to determine certain indicators, for example, indicators related to economic benefits, due to the loss of passenger flow data, the resulting indicator results are inaccurate. Summary of the Invention

[0003] Embodiments of this application provide a method, device, equipment, and storage medium for compensating passenger flow data, at least to solve the problem of passenger flow data loss in the prior art.

[0004] To achieve the above objective, the embodiments of this application adopt the following technical solutions:

[0005] In a first aspect, embodiments of this application provide a method for compensating passenger flow data. The method includes: determining that there is a lack of passenger flow data in a passenger flow equipment group within a first time interval; the passenger flow equipment group includes one or more devices for collecting passenger flow data; determining the time attribute of the first time interval, where the time attribute includes a time category and a time length; according to the time attribute of the first time interval, obtaining the passenger flow data of the passenger flow equipment group within a second time interval, where the second time interval has the same time attribute as the first time interval, and the time interval between the second time interval and the first time interval is less than a first preset threshold; predicting the missing passenger flow data of the passenger flow equipment group within the first time interval based at least on the passenger flow data of the passenger flow equipment group within the second time interval.

[0006] As can be seen from the above, the method for compensating passenger flow data provided by this application predicts the missing passenger flow data within the first time interval by obtaining the passenger flow data of the second time interval that is related to the passenger flow equipment group within the first time interval. The relationship between the second time interval and the first time interval is mainly characterized by the time category and the time length. The passenger flow data corresponding to different time categories varies greatly. Therefore, a second time interval with the same time category as the first time interval is selected to predict the missing passenger flow data. In this way, the accuracy of the predicted passenger flow data within the first time interval can be improved.

[0007] In a possible implementation, the missing passenger flow data of the passenger flow device group in the first time interval is determined at least based on the passenger flow data of the passenger flow device group in the second time interval, including: determining the missing passenger flow data of the passenger flow device group in the first time interval based on the passenger flow data of the passenger flow device group in the second time interval and the passenger flow change characteristics of the passenger flow device group; wherein the passenger flow change characteristics include at least one of a passenger flow trend characteristic and a passenger flow fluctuation characteristic; the passenger flow trend characteristic is used to characterize the change trend of passenger flow data over a period of time, and the passenger flow fluctuation characteristic is used to characterize the fluctuation of passenger flow data of the same time category over a period of time.

[0008] In a possible implementation, determining the missing passenger flow data of the passenger flow device group in the first time interval based on the passenger flow data of the passenger flow device group in the second time interval and the passenger flow change characteristics of the passenger flow device group includes: determining the predicted passenger flow data of the first time interval based on the passenger flow data of the passenger flow device group in the second time interval and the passenger flow change characteristics of the passenger flow device group; determining the missing passenger flow data of the passenger flow device group in the first time interval and in the preset time dimension based on the passenger flow data of the passenger flow device group in the second time interval, the distribution ratio in the preset time dimension, and the predicted passenger flow data of the first time interval.

[0009] In a possible implementation, determining the missing passenger flow data of the passenger flow device group in the first time interval and in the preset time dimension based on the distribution ratio of the passenger flow data of the passenger flow device group in the second time interval in the preset time dimension and the predicted passenger flow data of the first time interval includes: determining the passenger flow data of the passenger flow device group in the second time interval and in each preset time dimension; determining the distribution ratio of the passenger flow data in each preset time dimension according to the passenger flow data of the passenger flow device group in the second time interval and the passenger flow data in each preset time dimension; and allocating and calculating the predicted passenger flow data of the first time interval according to the distribution ratio of the passenger flow data in each preset time dimension to determine the missing passenger flow data of the passenger flow device group in the first time interval and in each preset time dimension.

[0010] In a possible implementation, determining the predicted passenger flow data of the first time interval based on the passenger flow data of the passenger flow device group in the second time interval and the passenger flow change characteristics of the passenger flow device group includes: determining the predicted passenger flow data of the first time interval by using a passenger flow data estimation model and the passenger flow data in the second time interval; wherein the passenger flow data estimation includes the corresponding relationship between input data and output data, the input data is the passenger flow data of the passenger flow device group in the second time interval and the passenger flow change characteristics of the passenger flow device group, and the output data is the missing passenger flow data of the passenger flow device group in the first time interval.

[0011] In a possible implementation, determining that there is missing passenger flow data for a passenger flow device group in a first time interval includes: querying the passenger flow data of the passenger flow device group in the first time interval; when the passenger flow data of the passenger flow device group in the first time interval is less than a second preset threshold, determining that there is missing passenger flow data for the passenger flow device group in the first time interval.

[0012] In a possible implementation, determining that there is missing passenger flow data for a passenger flow device group in a first time interval includes: receiving an indication message; wherein, the indication message is used to indicate that there is expected to be missing passenger flow data for the passenger flow device group in the first time interval; based on the indication message, querying the passenger flow data of the passenger flow device group in the first time interval; outputting the queried passenger flow data, and in response to a user operation, determining that there is missing passenger flow data for the passenger flow device in the first time interval.

[0013] In a possible implementation, the method further includes: outputting predicted passenger flow data.

[0014] In a possible implementation, after outputting the predicted passenger flow data, the method further includes: receiving a modification instruction; the modification instruction is used to indicate modifying the predicted passenger flow data; in response to the modification instruction, adjusting the predicted passenger flow data.

[0015] In a second aspect, an embodiment of the present application provides a compensation device for passenger flow data. The device includes a processing unit and an acquisition unit. Wherein, the processing unit is used to determine that there is missing passenger flow data for a passenger flow device group in a first time interval; the passenger flow device group includes one or more devices for collecting passenger flow data; the processing unit is further used to determine the time attribute of the first time interval, and the time attribute includes a time category and a time length, and the time category is determined based on the passenger flow volume; the acquisition unit is used to obtain the passenger flow data of the passenger flow device group in a second time interval according to the time attribute of the first time interval, the second time interval has the same time attribute as the first time interval, and the time interval between the second time interval and the first time interval is less than a first preset threshold; the processing unit is further used to at least based on the passenger flow data of the passenger flow device group in the second time interval, predict the missing passenger flow data of the passenger flow device group in the first time interval.

[0016] In a possible implementation, the processing unit is further used to determine the missing passenger flow data of the passenger flow device group in the first time interval based on the passenger flow data of the passenger flow device group in the second time interval and the passenger flow change characteristics of the passenger flow device group; wherein, the passenger flow change characteristics include at least one of a passenger flow trend characteristic and a passenger flow fluctuation characteristic.

[0017] In a possible implementation, the processing unit is further configured to determine the predicted passenger flow data for the first time interval; the processing unit is further configured to determine the missing passenger flow data of the passenger flow device group in the first time interval based on the distribution ratio of the passenger flow data of the passenger flow device group in the second time interval and the predicted passenger flow data of the first time interval.

[0018] In a possible implementation, the processing unit is further configured to query the passenger flow data of the passenger flow device group in the first time interval; the processing unit is further configured to determine that there is missing passenger flow data of the passenger flow device group in the first time interval when the passenger flow data of the passenger flow device group in the first time interval is less than the second preset threshold.

[0019] In a possible implementation, the obtaining unit is further configured to receive an indication message; wherein, the indication message is used to indicate that there is missing passenger flow data in the data of the expected passenger flow device group in the first time interval; the processing unit is further configured to query the passenger flow data of the passenger flow device group in the first time interval based on the indication message; the processing unit is further configured to output the queried passenger flow data and, in response to a user operation, determine that there is missing passenger flow data of the passenger flow device in the first time interval.

[0020] In a possible implementation, the processing unit is further configured to output the predicted passenger flow data.

[0021] In a possible implementation, the obtaining unit is further configured to receive a modification instruction; the modification instruction is used to indicate modifying the predicted passenger flow data; the processing unit is further configured to adjust the predicted passenger flow data in response to the modification instruction.

[0022] In a third aspect, an electronic device is provided, including: a processor; a memory for storing executable instructions of the processor; wherein, the processor is configured to execute the instructions to implement the passenger flow data compensation method provided in the first aspect as described above.

[0023] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions. When the instructions are run on a computer, the computer is caused to execute the passenger flow data compensation method provided in the first aspect as described above.

[0024] In a fifth aspect, the present application provides a computer program product which, when run on a computer, causes the computer to execute the passenger flow data compensation method provided in the first aspect as described above.

[0025] It should be noted that the above computer instructions may be stored in whole or in part on the first computer-readable storage medium. Among them, the first computer-readable storage medium may be packaged together with the processor of the access network terminal device or separately packaged from the processor of the access network terminal device, and the present application does not make any limitation in this regard.

[0026] For the descriptions of the second, third, fourth, and fifth aspects in this application, reference can be made to the detailed description of the first aspect; and for the beneficial effects described in the second, third, fourth, and fifth aspects, reference can be made to the analysis of the beneficial effects of the first aspect, which will not be elaborated here.

[0027] In this application, the above names do not constitute limitations on the electronic devices or functional modules themselves. In actual implementation, these terminal devices or functional modules may appear under other names. As long as the functions of each terminal device or functional module are similar to those in this application and fall within the scope of the claims of this application and their equivalent technologies.

[0028] These aspects or other aspects of this application will be made more concise and understandable in the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a schematic structural diagram of a compensation system for passenger flow data provided by an embodiment of this application;

[0030] Figure 2 is one of the schematic flowcharts of a method for compensating passenger flow data provided by an embodiment of this application;

[0031] Figure 3 is a schematic diagram of the statistics of passenger flow data provided by an embodiment of this application;

[0032] Figure 4 is the second schematic flowchart of a method for compensating passenger flow data provided by an embodiment of this application;

[0033] Figure 5 is the third schematic flowchart of a method for compensating passenger flow data provided by an embodiment of this application;

[0034] Figure 6 is the fourth schematic flowchart of a method for compensating passenger flow data provided by an embodiment of this application;

[0035] Figure 7A is the fifth schematic flowchart of a method for compensating passenger flow data provided by an embodiment of this application;

[0036] Figure 7B is the sixth schematic flowchart of a method for compensating passenger flow data provided by an embodiment of this application;

[0037] Figure 7C is the seventh schematic flowchart of a method for compensating passenger flow data provided by an embodiment of this application;

[0038] Figure 8 is a schematic diagram of the statistics of passenger flow data on a working day not adjacent to a holiday provided by an embodiment of this application;

[0039] Figure 9 It is a schematic diagram of passenger flow data statistics on holidays provided by an embodiment of the present application;

[0040] Figure 10 It is a schematic structural diagram of a compensation device for passenger flow data provided by an embodiment of the present application;

[0041] Figure 11 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application;

[0042] Figure 12 It is a schematic structural diagram of a computer program product provided by an embodiment of the present application. Detailed implementation manners

[0043] In the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific manner.

[0044] In order to facilitate a clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, words such as "first" and "second" are used to distinguish identical or similar items with basically the same functions and roles. Those skilled in the art can understand that the words such as "first" and "second" do not limit the quantity or the execution order.

[0045] Currently, for places with a large number of people such as large shopping malls, scenic spots and stations, multiple passenger flow cameras are usually arranged to record passenger flow data, and then the recorded passenger flow data is sent to the passenger flow system. In this process, due to reasons such as power outages and equipment failures, the passenger flow data recorded by the passenger flow system may be missing. If subsequent statistical calculations need to be based on the passenger flow data, incorrect statistical results will be obtained.

[0046] Based on the above problems, an embodiment of the present application provides a method for compensating passenger flow data. Through this method, the missing passenger flow data can be compensated to ensure the continuity of the passenger flow data and facilitate subsequent statistical analysis.

[0047] Next, the implementation manners of the embodiments of the present application will be described in detail with reference to the drawings.

[0048] The method for compensating passenger flow data provided by the embodiments of the present application can be applied to a passenger flow data compensation system. Figure 1 Shows a possible structure of the passenger flow data compensation system. As Figure 1As shown in the figure, the compensation system 10 for passenger flow data provided by the embodiments of the present application may include: an electronic device 11 and multiple collection devices 12 for collecting passenger flow data.

[0049] The electronic device 11 is connected to multiple collection devices 12 through any possible connection method such as a wired network or a wireless network. The multiple collection devices 12 collect passenger flow data in real time and send the collected passenger flow data to the electronic device 11 in real time or at regular intervals. After receiving the passenger flow data, the electronic device 11 displays and / or statistically analyzes the passenger flow data.

[0050] Among them, the electronic device 11 may be a tablet computer, a desktop type, a laptop, a notebook computer, a netbook, a virtual reality terminal, an augmented reality terminal, a wireless terminal in unmanned driving, etc. The multiple collection devices 12 may be devices for collecting the number of passenger flows, such as passenger flow cameras in shopping malls.

[0051] The electronic device 11 and any one of the multiple collection devices 12 may be integrated in one device, or may be located in two independent devices. The embodiments of the present application do not impose any restrictions on the positional relationship between the electronic device 11 and the multiple collection devices 12.

[0052] It should be noted that the compensation method for passenger flow data provided by the embodiments of the present application may be applied to an electronic device. The execution subject of the compensation method for passenger flow data provided by the embodiments of the present application may also be a compensation device for passenger flow data. The compensation device for passenger flow data may be an electronic device, or a central processing unit (CPU) in the electronic device, or a control module in the electronic device for executing the parking space detection method.

[0053] Next, taking the electronic device as an example, the compensation method for passenger flow data provided by the embodiments of the present application will be described in detail.

[0054] Please refer to Figure 2 , which is a flowchart of a compensation method for passenger flow data provided by the embodiments of the present application. The compensation method for passenger flow data may include S201 - S204.

[0055] S201. The electronic device determines that there is a lack of passenger flow data in the passenger flow device group within the first time interval.

[0056] Among them, the passenger flow device group includes one or more devices for collecting passenger flow data.

[0057] In a large shopping mall, multiple passenger flow cameras are usually installed. The passenger flow cameras are mainly used to record the number of people in the mall. One or more passenger flow cameras with common attributes can be analyzed centrally as a passenger flow device group. The common attributes can be: all are passenger flow cameras at the front door, all are passenger flow cameras at the back door, and all are passenger flow cameras in Store X of a certain brand, etc.

[0058] Exemplarily, the passenger flow device group can also be used in scenic spots, railway stations and other places where the number of people needs to be recorded. In Shopping Mall A, two passenger flow cameras in Store C are taken as a passenger flow device group. In Shopping Mall B, three passenger flow cameras in Store C are taken as a passenger flow device group. The device ID of each passenger flow camera in each passenger flow device group is recorded respectively.

[0059] After obtaining the passenger flow data recorded in the passenger flow device group, relevant business decisions can be made on the store or shopping mall corresponding to the passenger flow device group according to the amount of the passenger flow data. For example, if the passenger flow data of Store C in Shopping Mall A is significantly lower than that of Store C in Shopping Mall B, subsequent consideration will be given to closing Store C in Shopping Mall A.

[0060] S202. The electronic device determines the time attribute of the first time interval.

[0061] The time attribute includes time category and time length.

[0062] After determining that there is missing passenger flow data in the first time interval, the electronic device can obtain the time category of the first time interval and the specific time length of the first time interval based on the detailed data of the first time interval.

[0063] Among them, the time category can be divided into holidays, working days not adjacent to holidays, and working days adjacent to holidays. It can also be divided into holidays and working days. Or the time category can be determined based on the passenger flow, for example, divided into off-peak seasons and peak seasons, or divided into the first level, the second level and the third level, etc. The first level corresponds to the highest passenger flow data, and the third level corresponds to the lowest passenger flow data.

[0064] The time length can be determined according to the first time interval with missing passenger flow data. For example, if the first time interval with missing passenger flow data is from 7:00 to 9:00 on June 1st, then the time length is two hours corresponding to 7:00 - 9:00.

[0065] Exemplarily, see Figure 3, October 1st to October 7th are holidays, and the corresponding passenger flow data are close to those on Saturdays and Sundays (such as October 16th, October 17th, October 23rd, October 24th, October 30th, October 31st, etc.). The passenger flow data on Mondays, Tuesdays, Wednesdays and Thursdays (such as October 11th to October 14th, October 18th to October 21st and October 25th to October 28th) are close. The passenger flow data on Fridays (such as October 15th, October 22nd and October 29th) has increased significantly compared with the passenger flow data on Mondays, Tuesdays, Wednesdays and Thursdays.

[0066] Based on the above, we can see that the passenger flow data will be quite different if the time categories corresponding to the time intervals are different. When compensating the passenger flow data of a certain time interval, it is necessary to first determine the time category of the time interval. Predicting the passenger flow data of the missing time interval based on the passenger flow data of the time intervals of the same time category will be closer to the actual passenger flow data.

[0067] S203: The electronic device obtains the passenger flow data of the passenger flow device group in the second time interval according to the time attribute of the first time interval.

[0068] The second time interval has the same time attribute as the first time interval, and the time interval between the second time interval and the first time interval is less than a first preset threshold.

[0069] By obtaining a second time interval with the same time attribute as the first time interval, it is used as the basis for predicting the passenger flow data of the first time interval. At the same time, the time interval between the second time interval and the first time interval is limited. In this way, it is possible to avoid a time span that is too long and a huge change in the place monitored by the passenger flow equipment group. For example, if the area of ​​the place monitored by the passenger flow equipment group increases, the corresponding passenger flow data in the first time interval may also increase. This further avoids the passenger flow data of the predicted first time interval having a large error.

[0070] Exemplarily, the first preset threshold value may be 30 days. If the second time interval is before the first time interval, the interval between the time indicated by the maximum value of the second time interval and the time indicated by the minimum value of the first time interval shall not exceed one month. If the second time interval is after the first time interval, the interval between the time indicated by the minimum value of the second time interval and the time indicated by the maximum value of the first time interval shall not exceed one month. For example, the first time interval is 7:00-9:00 a.m. on June 1st. If the second time interval is before the first time interval, the maximum value of the second time interval shall not exceed 7:00 a.m. on May 1st. If the second time interval is after the first time interval, the minimum value of the second time interval shall not exceed 9:00 a.m. on July 1st.

[0071] S204. The electronic device predicts the missing passenger flow data of the passenger flow device group in the first time interval based at least on the passenger flow data of the passenger flow device group in the second time interval.

[0072] Combined with S203, if there is missing data in the first time interval (e.g., from 7 am to 9 am on June 1st) for the passenger flow device group, and the time category of the first time interval is a working day not adjacent to a holiday. Then a second time interval (e.g., from 7 am to 9 am on May 25th) that is a working day not adjacent to a holiday and has a time interval less than the first preset threshold with the first time interval can be obtained. Then, based on the passenger flow data of the second time interval (e.g., from 7 am to 9 am on May 25th), the missing passenger flow data in the first time interval (e.g., from 7 am to 9 am on June 1st) can be predicted.

[0073] After obtaining the passenger flow data of the passenger flow device group in the second time interval, the missing passenger flow data of the passenger flow device group in the first time interval can be predicted by using the passenger flow data prediction model and the passenger flow data in the second time interval. Specifically, the passenger flow data Xt predicted by the passenger flow data prediction model is Xt = U. Where Xt is the missing passenger flow data in the first time interval. U is the passenger flow data of the second time interval. That is, the missing passenger flow data in the first time interval can be directly equated to the passenger flow data of the second time interval.

[0074] Currently, most passenger flow cameras record passenger flow data in minutes as the time dimension. Therefore, the passenger flow data recorded every minute in the second time interval can be used as the passenger flow data for each minute in the first time interval.

[0075] Combined with the above Figure 2 , such as Figure 4 shown, the method further includes:

[0076] S205. The electronic device outputs the predicted passenger flow data.

[0077] After predicting the missing passenger flow data of the passenger flow device group in the first time interval, the electronic device can display the predicted passenger flow data on the interface of the passenger flow system. In this way, the interaction between the user and the passenger flow system is increased. Thus, the user experience is improved.

[0078] S206. The electronic device receives a modification instruction.

[0079] Wherein, the modification instruction is used to indicate modifying the predicted passenger flow data.

[0080] After the passenger flow system outputs the predicted passenger flow data, the user can view the predicted passenger flow data. If the user believes that the predicted passenger flow data is not accurate enough, the electronic device receives a modification instruction from the user in response to the user's trigger operation on the passenger flow system.

[0081] S207: The electronic device adjusts the predicted passenger flow data in response to the modification instruction.

[0082] In response to the user's modification instruction, the predicted passenger flow data for the first time interval is adjusted based on the user's wishes, and the adjusted passenger flow data for the first time interval is output. In this way, the user can revise the predicted passenger flow data again, thereby improving the flexibility of the system.

[0083] Combined with the above Figure 2 ,like Figure 5 As shown, the above S201 may include:

[0084] S2011. The electronic device queries the passenger flow data of the passenger flow device group in the first time interval.

[0085] Querying the passenger flow data of the passenger flow device group in the first time interval may be to check the accuracy of the passenger flow data after receiving each passenger flow data sent by the passenger flow camera. It may also be to receive a query request from a user and query based on the query conditions included in the query request. The query conditions may include passenger flow device group information and time interval information.

[0086] S2012: When the passenger flow data of the passenger flow device group in the first time interval is less than a second preset threshold, the electronic device determines that there is a lack of passenger flow data in the passenger flow device group in the first time interval.

[0087] After the query, the query result is analyzed. Specifically, the passenger flow data obtained by the query is compared with the second preset threshold. If the passenger flow data is less than the second preset threshold, it is considered that the passenger flow device has missing passenger flow data in the first time interval. Exemplarily, the second preset threshold can be 0.

[0088] Combined with the above Figure 2 ,like Figure 6 As shown, the above S201 may also include:

[0089] S2013. The electronic device receives instruction information.

[0090] The indication information is used to indicate that data of the estimated passenger flow device group within the first time interval contains missing passenger flow data.

[0091] After the passenger flow system estimates that the passenger flow device group has missing passenger flow data in the first time interval, it is necessary to inform the user of the missing passenger flow data. The notification method may be through indication information. Specifically, the indication information may be displayed on a display or played on a player. In this way, the user can be reminded that the passenger flow device group may have missing passenger flow data in the first time interval.

[0092] S2014. The electronic device queries the passenger flow data of the passenger flow device group within the first time interval based on the indication information.

[0093] After the user views the prompt information, the user will input query conditions in the passenger flow system according to the passenger flow device group information and time information included in the prompt information to query the corresponding passenger flow data.

[0094] S2015. The electronic device outputs the queried passenger flow data and determines that there is a lack of passenger flow data for the passenger flow device within the first time interval in response to the user operation.

[0095] In response to the user's query operation, the passenger flow system outputs the passenger flow data. After the user views the output passenger flow data, if the passenger flow system receives the user operation, it is determined that the user is aware of the situation that there is a lack of passenger flow data for the passenger flow device within the first time interval. Exemplarily, the user operation may be an operation to control the electronic device to perform passenger flow data compensation.

[0096] Combined with the above Figure 2 , such as Figure 7A shown, the above S204 may further include:

[0097] S2041. The electronic device determines the missing passenger flow data of the passenger flow device group within the first time interval based on the passenger flow data of the passenger flow device group within the second time interval and the passenger flow change characteristics of the passenger flow device group.

[0098] Among them, the passenger flow change characteristics include at least one of the passenger flow trend characteristics and the passenger flow fluctuation characteristics. The passenger flow trend characteristics are used to characterize the change trend of the passenger flow data over a period of time, and the passenger flow fluctuation characteristics are used to characterize the fluctuation of the passenger flow data of the same time category over a period of time.

[0099] The electronic device may also obtain the passenger flow change characteristics of the passenger flow device group and a random number. The passenger flow change characteristics include the passenger flow trend characteristics and the passenger flow fluctuation characteristics. The passenger flow trend characteristics are determined based on the historical passenger flow data over a relatively long period of time and are used to characterize the change trend of the passenger flow data over a period of time. For example, the change trend of the passenger flow data in the past month. The passenger flow fluctuation characteristics are determined based on the historical passenger flow data corresponding to a certain time category and are used to characterize the fluctuation of a specific time category over a period of time. For example, the fluctuation of the passenger flow data on weekdays not adjacent to holidays in the past month. The random value R is used to accommodate the influence of other accidental factors on the passenger flow data. The fluctuation range is 0.95 ≤ R ≤ 1.05.

[0100] Combined with S204, after obtaining the passenger flow fluctuation characteristics, the random value, and the passenger flow data of the second time interval (e.g., from 7:00 am to 9:00 am on May 25th), the passenger flow data prediction model can be used to predict the passenger flow data of the first time interval.

[0101] The passenger flow data Xt predicted by the passenger flow data prediction model is Xt = U × Tt × St × Rt.

[0102] Among them, Tt is the passenger flow trend feature, St is the passenger flow fluctuation feature, and Rt is a random value. Tt, St, and Rt are optional. For example, at least one of Tt, St, and Rt can be selected. Then the predicted passenger flow data can be Xt = U × Tt × Rt, or Xt = U × Tt × St, or Xt = U × Rt, etc. In this way, the problem of high error rate caused by manual compensation is avoided.

[0103] Combined with the above Figure 2 , such as Figure 7A shown, the above S204 may further include:

[0104] S2042. The electronic device determines the predicted passenger flow data for the first time interval.

[0105] Usually, the passenger flow system conducts passenger flow data statistics on a daily basis. To determine the predicted passenger flow data for the first time interval, the predicted passenger flow data for the third time interval corresponding to the first time interval in days can be determined first. For example, if the first time interval is from 7:00 to 9:00 on June 1st, the third time interval can be from 0:00 to 24:00 on June 1st.

[0106] Specifically, the electronic device can determine the predicted passenger flow data for the third time interval based on a time interval that has the same time category as the third time interval and a time interval whose time interval does not exceed the first preset threshold.

[0107] If the time category of the third time interval is a working day not adjacent to a holiday, the electronic device can select the passenger flow data of the fourth time interval that has the same time category as the third time interval, the same time length, and a time range within one month as the predicted passenger flow data for the third time interval. The passenger flow data of the fourth time interval can be the passenger flow data of customers entering the mall or the passenger flow data of customers leaving the mall. This application does not limit this.

[0108] Alternatively, the electronic device can also select the passenger flow data corresponding to all working days not adjacent to holidays within the past month, sum them up and take the average to obtain the predicted passenger flow data for the third time interval.

[0109] After obtaining the predicted passenger flow data for the third time interval, the predicted passenger flow data for the first time interval can be obtained by subtracting the actual passenger flow data for other time intervals except the first time interval from the predicted passenger flow data for the third time interval. For example, after obtaining the predicted passenger flow data for the third time interval, based on the predicted passenger flow data for the third time interval (e.g., 0:00 - 24:00 on June 1st), subtract the actual data for the time intervals without missing passenger flow data (e.g., 0:00 - 7:00 on June 1st and 9:00 - 24:00 on June 1st) to obtain the predicted passenger flow data for the first time interval (e.g., 7:00 - 9:00 on June 1st).

[0110] S2043. The electronic device determines the missing passenger flow data of the passenger flow device group in the first time interval based on the distribution ratio of the passenger flow data of the passenger flow device group in the second time interval and the predicted passenger flow data of the first time interval.

[0111] See Figure 8 and Figure 9 , Figure 8 is the distribution of passenger flow data for a day with the time category being a working day not adjacent to a holiday. Figure 9 is the distribution of passenger flow data for a day with the time category being a holiday. From Figure 8 and Figure 9 , it can be seen that there are obvious differences in the passenger flow data corresponding to different time categories. For example, Figure 8 in the passenger flow data of a working day not adjacent to a holiday, the distribution is relatively flat during normal working hours (e.g., 9:00 am - 12:00 pm, 2:00 pm - 5:00 pm), and there is relatively more passenger flow data outside normal working hours. While Figure 9 in the passenger flow data of a holiday, there is also more distribution during normal working hours. Therefore, this application can calculate the distribution ratio of passenger flow data in the second time interval in minutes based on the normal distribution, and use the distribution ratio to determine the passenger flow data per minute in the first time interval.

[0112] Specifically, the electronic device can directly obtain the passenger flow data of the passenger flow device group in the second time interval in the passenger flow system. If the passenger flow system records passenger flow data on a daily basis, then the electronic device can obtain the passenger flow data for the day corresponding to the second time interval, and then find the passenger flow data in the second time interval from the passenger flow data for the day.

[0113] Then, according to the proportion of passenger flow data per minute in the second time interval (e.g., 7:00 - 9:00 on May 25th) and the predicted passenger flow data in the first time interval (e.g., 7:00 - 9:00 on June 1st), determine the passenger flow data per minute in the first time interval (e.g., 7:00 - 9:00 on June 1st). By compensating the passenger flow data per minute, the continuity of passenger flow data in the passenger flow system can be ensured, thereby improving the usability of the passenger flow system.

[0114] Combined with the above Figure 2 , as Figure 7B shown, the above S2041 may further include:

[0115] S20411. The electronic device determines the predicted passenger flow data for the first time interval based on the passenger flow data of the passenger flow device group within the second time interval and the passenger flow change characteristics of the passenger flow device group.

[0116] The electronic device can calculate the predicted passenger flow data for the first time interval according to the total passenger flow data of the passenger flow device group within the second time interval, and then in combination with the passenger flow trend characteristics and passenger flow fluctuation characteristics of the passenger flow device group. Among them, the predicted passenger flow data for the first time interval is the total predicted passenger flow data. For the detailed content of the passenger flow trend characteristics and passenger flow fluctuation characteristics, refer to S2041.

[0117] Optionally, S20411 further includes S204111.

[0118] S204111. The electronic device determines the predicted passenger flow data for the first time interval by using the passenger flow data prediction model and the passenger flow data within the second time interval.

[0119] Among them, the passenger flow data prediction model includes the corresponding relationship between the input data and the output data. The input data is the passenger flow data of the passenger flow device group within the second time interval and the passenger flow change characteristics of the passenger flow device group, and the output data is the missing passenger flow data of the passenger flow device group within the first time interval.

[0120] Specifically, the passenger flow data prediction model can be used to predict the predicted passenger flow data for the first time interval. The predicted passenger flow data Xt for the first time interval predicted by the passenger flow data prediction model can be determined by the product of the passenger flow data U within the second time interval, the passenger flow trend characteristic Tt, and the passenger flow fluctuation characteristic St. That is, Xt = U × Tt × St. The predicted passenger flow data Xt for the first time interval predicted by the passenger flow data prediction model can also be determined by the product of the passenger flow data U within the second time interval, the passenger flow trend characteristic Tt, the passenger flow fluctuation characteristic St, and the random value Rt, that is, Xt = U × Tt × St × Rt.

[0121] S20412. The electronic device determines the missing passenger flow data of the passenger flow device group within the first time interval and on the preset time dimension based on the passenger flow data of the passenger flow device group within the second time interval, the distribution ratio on the preset time dimension, and the predicted passenger flow data for the first time interval.

[0122] Figure 8 is a statistical schematic diagram of the passenger flow data for a working day not adjacent to a holiday, Figure 9Schematic diagram of passenger flow data statistics during holidays. It can be observed that there are obvious differences in the passenger flow data corresponding to different time categories. Therefore, in this application, the missing passenger flow data per minute in the first time interval is determined based on the second time interval that has the same time category as the first time interval.

[0123] Specifically, the electronic device can find the passenger flow data within the second time interval in the passenger flow system. Then, based on the proportion of the passenger flow data in the preset time dimension (per minute) within the second time interval (e.g., from 7:00 to 9:00 on May 25th) and the predicted passenger flow data in the first time interval (e.g., from 7:00 to 9:00 on June 1st), the passenger flow data in the preset time dimension (per minute) within the first time interval (e.g., from 7:00 to 9:00 on June 1st) is determined. Among them, the preset time dimension can be in minutes, or in hours or days, and this application does not limit the comparison.

[0124] Combined with the above Figure 2 , such as Figure 7C shown, the above S2043 may further include:

[0125] S204121. The electronic device determines the passenger flow data of the passenger flow device group within the second time interval and for each preset time dimension.

[0126] S204122. The electronic device determines the distribution ratio of the passenger flow data for each preset time dimension based on the passenger flow data of the passenger flow device group within the second time interval and the passenger flow data for each preset time dimension.

[0127] S204123. The electronic device performs distribution calculation on the predicted passenger flow data of the first time interval according to the distribution ratio of the passenger flow data for each preset time dimension, and determines the missing passenger flow data of the passenger flow device group within the first time interval and for each preset time dimension.

[0128] Exemplarily, in combination with S2043, when it is determined that there is missing passenger flow data of the passenger flow device group within the first time interval and for the preset time dimension, first, a time interval that has the same time category as the first time interval is determined. Since the time category of the first time interval is a working day that is not adjacent to a holiday, the second time interval is determined according to this category. For example, the first time interval is from 7:00 to 9:00 on June 1st, and the second time interval is from 7:00 to 9:00 on May 25th.

[0129] When the preset time dimension within the second time interval is in minutes, after the electronic device obtains the passenger flow data within the second time interval, it is also necessary to count the passenger flow data per minute within the second time interval. That is, the passenger flow data at 7:01 on May 25th, the passenger flow data at 7:02, …, the passenger flow data at 8:58, and the passenger flow data at 8:59.

[0130] After obtaining the passenger flow data for each minute within the second time interval, combined with the total passenger flow data within the second time interval, calculate the distribution proportion of the passenger flow data for each minute in the second time interval. For example, the total passenger flow data within the second time interval is 2000, the passenger flow data at 7:01 on May 25th is 10, the passenger flow data at 7:02 is 50, …, the passenger flow data at 8:58 is 20, and the passenger flow data at 8:59 is 30. Based on the total passenger flow within the second time interval and the specific passenger flow data for each minute, the distribution proportion of the passenger flow data for each minute can be obtained. That is, the distribution proportion of the passenger flow data at 7:01 on May 25th is 0.005, the distribution proportion of the passenger flow data at 7:02 is 0.025, …, the distribution proportion of the passenger flow data at 8:58 is 0.01, and the distribution proportion of the passenger flow data at 8:59 is 0.015.

[0131] After determining the distribution proportion of the passenger flow data for each minute, based on the predicted passenger flow data in the first time interval predicted in advance and the distribution proportion of the passenger flow data on the preset time dimension within the second time interval, calculate the missing passenger flow data on the preset time dimension within the first time interval. Specifically, multiply the predicted passenger flow data in the first time interval by the distribution proportion of the passenger flow data for each time dimension within the second time interval, and the missing passenger flow data for each preset time dimension within the first time interval can be calculated. For example, the predicted passenger flow data in the first time interval is 2400, then the missing passenger flow data at 7:01 on June 1st is 2400 * 0.005 = 12, the missing passenger flow data at 7:02 is 2400 * 0.025 = 60, …, the distribution proportion of the passenger flow data at 8:59 is 2400 * 0.01 = 24, and the distribution proportion of the passenger flow data at 9:00 is 2400 * 0.015 = 36.

[0132] The above introduced the solution provided by the embodiments of the present application from the perspective of the method. To implement the above functions, it includes the corresponding hardware structure and / or software module for executing each function. Those skilled in the art should easily realize that, combining the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0133] Next, in combination with Figure 10A compensation device for passenger flow data provided by an embodiment of the present application will be described in detail. It should be understood that the description of the device embodiment corresponds to the description of the method embodiment. Therefore, for the content not described in detail, reference can be made to the above method embodiment. For the sake of brevity, it will not be repeated here.

[0134] Figure 10 FIG. is a schematic structural diagram of a compensation device for passenger flow data provided by an embodiment of the present application. As Figure 10 shown, the first node device is used to execute Figures 2 - 7C any one of the passenger flow data compensation methods shown in

[0135] The processing unit 102 is used to determine that there is missing passenger flow data in the passenger flow device group within the first time interval; the passenger flow device group includes one or more devices for collecting passenger flow data; for example, in combination with Figure 2 , the processing unit 102 can be used to execute S201.

[0136] The processing unit 102 is further used to determine the time attribute of the first time interval, and the time attribute includes a time category and a time length, and the time category is determined based on the passenger flow volume; for example, in combination with Figure 2 , the processing unit 102 can be used to execute S202.

[0137] The obtaining unit is used to obtain the passenger flow data of the passenger flow device group within the second time interval according to the time attribute of the first time interval, the second time interval has the same time attribute as the first time interval, and the time interval between the second time interval and the first time interval is less than the first preset threshold; for example, in combination with Figure 2 , the obtaining unit 101 can be used to execute S203.

[0138] The processing unit 102 is further used to predict the missing passenger flow data of the passenger flow device group within the first time interval at least based on the passenger flow data of the passenger flow device group within the second time interval. For example, in combination with Figure 2 , the processing unit 102 can be used to execute S204.

[0139] Optionally, the processing unit 102 is further used to determine the missing passenger flow data of the passenger flow device group within the first time interval based on the passenger flow data of the passenger flow device group within the second time interval and the passenger flow change characteristics of the passenger flow device group; wherein, the passenger flow change characteristics include at least one of a passenger flow trend characteristic and a passenger flow fluctuation characteristic. For example, in combination with Figure 7A , the processing unit 102 can be used to execute S2041.

[0140] Optionally, the processing unit 102 is further used to determine the predicted passenger flow data of the first time interval; for example, in combination with Figure 7A, the processing unit 102 can be used to execute S2042.

[0141] The processing unit 102 is further configured to determine the missing passenger flow data of the passenger flow device group in the first time interval based on the distribution ratio of the passenger flow data of the passenger flow device group in the second time interval and the predicted passenger flow data in the first time interval. For example, in combination with Figure 7A , the processing unit 102 can be used to execute S2043.

[0142] Optionally, the processing unit 102 is further configured to query the passenger flow data of the passenger flow device group in the first time interval; for example, in combination with Figure 5 , the processing unit 102 can be used to execute S2011.

[0143] The processing unit 102 is further configured to determine that there is missing passenger flow data in the passenger flow device group in the first time interval when the passenger flow data of the passenger flow device group in the first time interval is less than the second preset threshold. For example, in combination with Figure 5 , the processing unit 102 can be used to execute S2012.

[0144] Optionally, the obtaining unit 101 is further configured to receive an indication message; wherein the indication message is used to indicate that there is missing passenger flow data in the expected data of the passenger flow device group in the first time interval; for example, in combination with Figure 6 , the obtaining unit 101 can be used to execute S2013.

[0145] The processing unit 102 is further configured to query the passenger flow data of the passenger flow device group in the first time interval based on the indication message; for example, in combination with Figure 6 , the processing unit 102 can be used to execute S2014.

[0146] The processing unit 102 is further configured to output the queried passenger flow data and, in response to a user operation, determine that there is missing passenger flow data in the passenger flow device in the first time interval. For example, in combination with Figure 6 , the processing unit 102 can be used to execute S2015.

[0147] Optionally, the processing unit 102 is further configured to output the predicted passenger flow data. For example, in combination with Figure 4 , the processing unit 102 can be used to execute S205.

[0148] Optionally, the obtaining unit is further configured to receive a modification instruction; the modification instruction is used to indicate modifying the predicted passenger flow data; for example, in combination with Figure 4 , the obtaining unit 101 can be used to execute S206.

[0149] The processing unit 102 is further configured to adjust the predicted passenger flow data in response to the modification instruction. For example, in combination with Figure 4, the processing unit 102 can be used to execute S207.

[0150] Of course, the parking space detection device provided by the embodiments of the present application includes but is not limited to the above modules. For example, the search intention determination device may further include a storage module 103. The storage module 103 can be used to store the program code of the search intention determination device, and can also be used to store the data generated during the operation of the search intention determination device, such as the data in the write request.

[0151] Figure 11 FIG. is a schematic structural diagram of an electronic device provided by an embodiment of the present application. The electronic device may include: at least two processors 111, a memory 112, a communication interface 113, and a communication bus 114.

[0152] Next, in conjunction with Figure 11 Each component of the terminal device overload detection device will be specifically introduced:

[0153] Among them, the processor 111 is the control center of the terminal device overload detection device, which can be a single processor or a collective term for multiple processing elements. For example, the processor 111 is a central processing unit (CPU), or can be an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application, such as: one or more DSPs, or one or more field programmable gate arrays (FPGAs).

[0154] In a specific implementation, as an embodiment, the processor 111 may include one or more CPUs, such as Figure 11 The CPU0 and CPU1 shown in Figure 11 Moreover, as an embodiment, the terminal device overload detection device may include multiple processors, such as

[0155] The memory 112 can be a read-only memory (ROM) or other types of static storage terminal devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage terminal devices that can store information and instructions, or can also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage terminal devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 112 can exist independently and be overloaded detected with the processor 111 through the communication bus 114. The memory 112 can also be integrated with the processor 111.

[0156] In a specific implementation, the memory 112 is used to store the data in this application and execute the software program of this application. The processor 111 can execute various functions of the air conditioner by running or executing the software program stored in the memory 112 and calling the data stored in the memory 112.

[0157] The communication interface 113 uses any device such as a transceiver to communicate with other terminal devices or communication networks, such as a radio access network (RAN), a wireless local area network (WLAN), terminal devices, the cloud, etc. The communication interface 113 can include an acquisition unit to implement the acquisition function and a sending unit to implement the sending function.

[0158] The communication bus 114 can be an industry standard architecture (ISA) bus, a peripheral component interconnect (PCI) bus, or an extended industry standard architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 11 only a thick line is used to represent it here, but it does not mean that there is only one bus or one type of bus.

[0159] Another embodiment of the present application further provides a computer-readable storage medium storing instructions that, when run on a computer, cause the computer to execute the method shown in the above method embodiments.

[0160] In some embodiments, the disclosed method may be implemented as computer program instructions encoded in a computer-readable storage medium or encoded on other non-transitory media or articles in a machine-readable format.

[0161] Figure 12 Schematically shows a conceptual partial view of a computer program product provided by an embodiment of the present application. The computer program product includes a computer program for executing a computer process on a computing terminal device.

[0162] In one embodiment, the computer program product is provided using a signal-bearing medium 1210. The signal-bearing medium 1210 may include one or more program instructions that, when run by one or more processors, may provide the functions or portions of the functions described above for Figure 2 Therefore, for example, referring to the embodiment shown in Figure 2 , one or more features of S201 - S204 may be borne by one or more instructions associated with the signal-bearing medium 1210. Additionally, Figure 12 The program instructions in

[0163] In some examples, the signal-bearing medium 1210 may include a computer-readable medium 1211, such as but not limited to, a hard disk drive, a compact disc (CD), a digital video disc (DVD), a digital tape, a memory, a read-only memory (ROM), or a random access memory (RAM), and so on.

[0164] In some embodiments, the signal-bearing medium 1210 may include a computer-recordable medium 1212, such as but not limited to, a memory, a read / write (R / W) CD, an R / W DVD, and so on.

[0165] In some embodiments, the signal-bearing medium 1210 may include a communication medium 1213, such as but not limited to, a digital and / or analog communication medium (e.g., an optical fiber cable, a waveguide, a wired communication link, a wireless communication link, and so on).

[0166] The signal-bearing medium 1210 may be conveyed by a wireless form of the communication medium 1213. One or more program instructions may be, for example, computer-executable instructions or logic-implemented instructions.

[0167] In some examples, such as for Figure 2 the write data device described, may be configured to provide various operations, functions, or actions in response to one or more program instructions via a computer-readable medium 1211, a computer-recordable medium 1212, and / or a communication medium 1213.

[0168] From the description of the above embodiments, those skilled in the art can clearly understand that for the convenience and conciseness of description, only the above division of each functional module is used as an example. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

[0169] In several embodiments provided by the present application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point, the mutual coupling or direct coupling or communication overload detection shown or discussed can be through some interfaces. The indirect coupling or communication overload detection of the device or unit can be in electrical, mechanical or other forms.

[0170] The units described as separate components may or may not be physically separated. The components shown as units may be a physical unit or multiple physical units, that is, they may be located in one place, or they may be distributed to multiple different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0171] In addition, each functional unit in the various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0172] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiments of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to enable a terminal device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps of the methods of the various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0173] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for compensating passenger flow data, characterized in that, Including: Determine that there is missing passenger flow data in the passenger flow device group within the first time interval; The passenger flow device group includes one or more devices for collecting passenger flow data; Determine the time attributes of the first time interval, where the time attributes include time categories and time lengths; According to the time attributes of the first time interval, obtain the passenger flow data of the passenger flow device group within the second time interval, where the second time interval has the same time attributes as the first time interval, and the time interval between the second time interval and the first time interval is less than the first preset threshold; Determine the missing passenger flow data of the passenger flow device group within the first time interval at least based on the passenger flow data of the passenger flow device group within the second time interval; The determining the missing passenger flow data of the passenger flow device group within the first time interval at least based on the passenger flow data of the passenger flow device group within the second time interval includes: Based on the passenger flow data of the passenger flow device group within the second time interval and the passenger flow change characteristics of the passenger flow device group, determine the predicted passenger flow data for the first time interval; where the passenger flow change characteristics include at least one of a passenger flow trend characteristic and a passenger flow fluctuation characteristic; the passenger flow trend characteristic is used to characterize the change trend of passenger flow data over a period of time, and the passenger flow fluctuation characteristic is used to characterize the fluctuation of passenger flow data of the same time category over a period of time; Based on the distribution ratio of the passenger flow data of the passenger flow device group within the second time interval in the preset time dimension and the predicted passenger flow data for the first time interval, determine the missing passenger flow data of the passenger flow device group within the first time interval in the preset time dimension.

2. The method according to claim 1, wherein The determining the missing passenger flow data of the passenger flow device group within the first time interval in the preset time dimension based on the distribution ratio of the passenger flow data of the passenger flow device group within the second time interval in the preset time dimension and the predicted passenger flow data for the first time interval includes: Determine the passenger flow data of the passenger flow device group within the second time interval in each preset time dimension; According to the passenger flow data of the passenger flow device group within the second time interval and the passenger flow data in each preset time dimension, determine the distribution ratio of the passenger flow data in each preset time dimension; Perform distribution calculation on the predicted passenger flow data for the first time interval according to the distribution ratio of the passenger flow data in each preset time dimension, and determine the missing passenger flow data of the passenger flow device group within the first time interval in each preset time dimension.

3. The method according to claim 1, wherein The determining the predicted passenger flow data for the first time interval based on the passenger flow data of the passenger flow device group within the second time interval and the passenger flow change characteristics of the passenger flow device group includes: Using the passenger flow data prediction model and the passenger flow data within the second time interval, determine the predicted passenger flow data for the first time interval; wherein, the passenger flow data prediction model includes the corresponding relationship between input data and output data, the input data is the passenger flow data of the passenger flow device group within the second time interval and the passenger flow change characteristics of the passenger flow device group, and the output data is the missing passenger flow data of the passenger flow device group within the first time interval.

4. The method according to any one of claims 1 to 3, characterized in that The method further includes: Output the predicted passenger flow data; Receive a modification instruction; the modification instruction is used to indicate modifying the predicted passenger flow data; In response to the modification instruction, adjust the predicted passenger flow data.

5. A compensation device for passenger flow data, characterized in that, It includes: A processing unit, configured to determine that there is missing passenger flow data for the passenger flow device group within the first time interval; the passenger flow device group includes one or more devices for collecting passenger flow data; The processing unit is further configured to determine the time attribute of the first time interval, where the time attribute includes a time category and a time length, and the time category is determined based on the passenger flow volume; An acquisition unit, configured to acquire the passenger flow data of the passenger flow device group within the second time interval according to the time attribute of the first time interval, where the second time interval has the same time attribute as the first time interval, and the time interval between the second time interval and the first time interval is less than a first preset threshold; The processing unit is further configured to predict the missing passenger flow data of the passenger flow device group within the first time interval at least based on the passenger flow data of the passenger flow device group within the second time interval; The determining the missing passenger flow data of the passenger flow device group within the first time interval at least based on the passenger flow data of the passenger flow device group within the second time interval includes: Based on the passenger flow data of the passenger flow device group within the second time interval and the passenger flow change characteristics of the passenger flow device group, determine the predicted passenger flow data for the first time interval; wherein, the passenger flow change characteristics include at least one of a passenger flow trend characteristic and a passenger flow fluctuation characteristic; the passenger flow trend characteristic is used to characterize the change trend of passenger flow data over a period of time, and the passenger flow fluctuation characteristic is used to characterize the fluctuation of passenger flow data of the same time category over a period of time; Based on the distribution ratio of the passenger flow data of the passenger flow device group within the second time interval in a preset time dimension and the predicted passenger flow data for the first time interval, determine the missing passenger flow data of the passenger flow device group within the first time interval in the preset time dimension.

6. The device according to claim 5, wherein The processing unit is further configured to query the passenger flow data of the passenger flow device group within the first time interval; The processing unit is further configured to determine that there is missing passenger flow data for the passenger flow device group within the first time interval when the passenger flow data of the passenger flow device group within the first time interval is less than a second preset threshold; The acquisition unit is further configured to receive an indication message; wherein, the indication message is used to indicate that it is expected that there is missing passenger flow data for the data of the passenger flow device group within the first time interval; The processing unit is further configured to query the passenger flow data of the passenger flow device group within the first time interval based on the indication information; The processing unit is further configured to output the queried passenger flow data and determine that there is a lack of passenger flow data of the passenger flow device within the first time interval in response to a user operation; The processing unit is further configured to output the predicted passenger flow data; The acquisition unit is further configured to receive a modification instruction; the modification instruction is used to indicate to modify the predicted passenger flow data; The processing unit is further configured to adjust the predicted passenger flow data in response to the modification instruction.

7. An electronic device, characterized in that, Comprising: A processor; A memory for storing executable instructions of the processor; Wherein, the processor is configured to execute the instructions to implement the method according to any one of claims 1-4.

8. A computer-readable storage medium, characterized in that, Instructions are stored, which when executed by a data processor, cause the data processor to execute the method according to any one of claims 1-4.

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