Data processing method and device, equipment and storage medium

Abstract

The application provides a data processing method, device and equipment and a storage medium, and is applied to various scenes such as cloud technology, artificial intelligence, a map field, intelligent transportation, auxiliary driving and a vehicle-mounted device. The method comprises the following steps: in response to an environment detection request, obtaining to-be-detected environment data; performing environment detection on the to-be-detected environment data to obtain a first environment detection result of the to-be-detected environment data; in the case where the first environment detection result meets a preset condition, obtaining position information of a device that collects the to-be-detected environment data; determining a target grid corresponding to the position information; and sending a service control instruction to a target device associated with the target grid, so that the target device performs a service operation in response to the service control instruction. The scheme of the embodiment of the application can improve the efficiency and accuracy of service task scheduling.

Description

Technical Field

[0001] This application belongs to the field of computer technology, and specifically relates to a data processing method, apparatus, device and storage medium. Background Technology

[0002] Currently, driving trajectories are typically recorded by a large number of vehicle-mounted terminals, such as dashcams, and uploaded to a server. The server then schedules business tasks to enable the vehicle-mounted terminals to perform corresponding business operations (for example, the server schedules road task collection to enable the vehicle-mounted terminals to perform data collection).

[0003] In related technologies, weather conditions for a city are obtained by calling weather forecasts from weather networks, thereby scheduling business tasks and enabling vehicle terminals to perform corresponding business operations. However, weather updates from weather networks are slow and delayed, resulting in inconsistencies with actual weather conditions. Furthermore, weather networks can only provide accuracy down to the city or district level, which is too granular and leads to low efficiency and accuracy in business task scheduling. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a data processing method, apparatus, device, and storage medium.

[0005] On the one hand, this application proposes a data processing method, the method comprising:

[0006] In response to an environmental monitoring request, acquire data on the environment to be monitored;

[0007] Environmental detection is performed on the environmental data to be detected to obtain a first environmental detection result for the environmental data to be detected.

[0008] If the first environmental detection result meets the preset conditions, obtain the location information of the device that collects the environmental data to be detected;

[0009] Determine the target grid corresponding to the location information;

[0010] Send a service control command to the target device associated with the target mesh, so that the target device responds to the service control command and performs service operations.

[0011] On the other hand, embodiments of this application provide a data processing method applied to a device where the navigation route includes a target grid and the distance between the device and the target grid is greater than a preset distance threshold. The method includes:

[0012] When the environmental detection result of the environmental data to be detected meets the preset conditions, an environmental early warning instruction carrying the environmental prediction result corresponding to the target grid is received; the environmental prediction result is determined based on the environmental detection result, the environmental detection result is obtained by performing environmental detection on the environmental data to be detected, the environmental data to be detected is the data obtained in response to the environmental detection request, and the target grid is determined based on the location information of the device that collects the environmental data to be detected;

[0013] In response to the environmental warning command, the environmental prediction results and / or the corresponding environmental warning information are displayed in the navigation route of the navigation interface.

[0014] On the other hand, embodiments of this application provide a data processing apparatus, the apparatus comprising:

[0015] The module for acquiring environmental data to be detected is used to acquire environmental data in response to environmental detection requests.

[0016] The first environmental detection result determination module is used to perform environmental detection on the environmental data to be detected and obtain the first environmental detection result of the environmental data to be detected.

[0017] The location information acquisition module is used to acquire the location information of the device that collects the environmental data to be detected when the first environmental detection result meets the preset conditions.

[0018] The target grid determination module is used to determine the target grid corresponding to the location information;

[0019] The service control instruction sending module is used to send service control instructions to the target device associated with the target mesh, so that the target device responds to the service control instructions and performs service operations.

[0020] On the other hand, embodiments of this application provide a data processing apparatus applied to a device in which a navigation route includes a target grid and the distance between the device and the target grid is greater than a preset distance threshold. The apparatus includes:

[0021] An environmental early warning instruction receiving module is used to receive an environmental early warning instruction carrying the environmental prediction result corresponding to the target grid when the environmental detection result of the environmental data to be detected meets preset conditions; the environmental prediction result is determined based on the environmental detection result, the environmental detection result is obtained by performing environmental detection on the environmental data to be detected, the environmental data to be detected is data obtained in response to an environmental detection request, and the target grid is determined based on the location information of the device that collects the environmental data to be detected;

[0022] The environmental early warning information display module is used to respond to the environmental early warning command and display the environmental prediction results and / or the environmental early warning information corresponding to the environmental prediction results in the navigation route of the navigation interface.

[0023] On the other hand, this application proposes an electronic device for data processing, the electronic device including a processor and a memory, the memory storing at least one instruction or at least one program, the at least one instruction or at least one program being loaded and executed by the processor to implement the data processing method as described above.

[0024] On the other hand, this application proposes a computer-readable storage medium storing at least one instruction or at least one program, which is loaded and executed by a processor to implement the data processing method as described above.

[0025] On the other hand, this application proposes a computer program product that, when executed by a processor, implements the data processing method as described above.

[0026] The data processing method, apparatus, device, and storage medium proposed in this application, in response to an environmental detection request, acquires environmental data to be detected; performs environmental detection on the environmental data to be detected to obtain a first environmental detection result; if the first environmental detection result meets preset conditions, acquires the location information of the device that collected the environmental data to be detected; determines the target grid corresponding to the location information; and sends a service control command to the target device associated with the target grid, so that the target device responds to the service control command and performs service operations. This realizes the scheduling of service tasks based on the real environmental detection result of the environmental data to be detected. Since the environmental detection result can truly reflect the environmental conditions within the target grid, there will be no discrepancy between the environmental data and the real environmental data, and there will be no delay in environmental data updates, thereby improving the efficiency and accuracy of service task scheduling. Attached Figure Description

[0027] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a schematic diagram illustrating the implementation environment of a data processing method according to an exemplary embodiment.

[0029] Figure 2This is a flowchart illustrating a data processing method according to an exemplary embodiment.

[0030] Figure 3 This is a flowchart illustrating an example of acquiring data about an environment to be detected, according to an exemplary embodiment.

[0031] Figure 4 This is a flowchart illustrating an environmental detection process based on an exemplary embodiment, in which environmental detection is performed on environmental data to be detected to obtain a first environmental detection result of the environmental data to be detected.

[0032] Figure 5 This is a schematic diagram illustrating reflective shading environmental data in rainy weather according to an exemplary embodiment.

[0033] Figure 6 This is a schematic diagram illustrating snow cover environment data according to an exemplary embodiment.

[0034] Figure 7 This is a schematic diagram illustrating foggy and blurred environmental data according to an exemplary embodiment.

[0035] Figure 8 This is a flowchart illustrating another environmental detection process for environmental data to be detected, according to an exemplary embodiment, to obtain a first environmental detection result for the environmental data to be detected.

[0036] Figure 9 This is a flowchart illustrating a method for predicting real weather within a target grid, according to an exemplary embodiment.

[0037] Figure 10 This is a flowchart illustrating a method for determining a target grid corresponding to location information according to an exemplary embodiment.

[0038] Figure 11 This is a flowchart illustrating, according to an exemplary embodiment, the updating of the state of a target mesh from a preset state to a non-preset state.

[0039] Figure 12 This is a flowchart illustrating, according to an exemplary embodiment, the updating of the state of a target mesh from a preset state to the aforementioned non-preset state.

[0040] Figure 13 This is a schematic diagram illustrating the display of environmental prediction results in a navigation route on a navigation interface, according to an exemplary embodiment.

[0041] Figure 14 This is a schematic diagram illustrating, according to an exemplary embodiment, the display of environmental early warning information corresponding to environmental prediction results in a navigation route of a navigation interface.

[0042] Figure 15This is a flowchart illustrating another data processing method according to an exemplary embodiment.

[0043] Figure 16 This is a schematic diagram illustrating an updated navigation route and showing the updated navigation route according to an exemplary embodiment.

[0044] Figure 17 This is a block diagram of a data processing apparatus according to an exemplary embodiment.

[0045] Figure 18 This is a block diagram of a data processing apparatus according to an exemplary embodiment.

[0046] Figure 19 This is a hardware structure block diagram of a data processing server provided according to an exemplary embodiment. Detailed Implementation

[0047] Artificial intelligence (AI) is the theory, methods, technology, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results. In other words, AI is a comprehensive technology within computer science that attempts to understand the essence of intelligence and produce a new kind of intelligent machine that can react in a way similar to human intelligence. AI studies the design principles and implementation methods of various intelligent machines, enabling them to possess the functions of perception, reasoning, and decision-making.

[0048] Artificial intelligence (AI) is a comprehensive discipline encompassing a wide range of fields, including both hardware and software technologies. Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies primarily include computer vision, speech processing, natural language processing, and machine learning / deep learning.

[0049] Specifically, in this embodiment of the application, the process of inputting the environmental data to be detected into multiple environmental classification networks to obtain the environmental classification results of the environmental data to be detected belonging to different environmental categories under each environmental classification network involves machine learning technology.

[0050] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0051] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0052] First, the technical terms involved in the embodiments of this application will be introduced:

[0053] Equipment crowdsourcing: A method of collecting data for road tasks by using a large number of mainstream in-vehicle terminals, such as dashcams, to record driving data and upload driving trajectory information.

[0054] Grid: Dividing urban administrative divisions into unit grids according to certain standards for task management and scheduling. It is a more refined first-level regional management unit than a city.

[0055] Figure 1 This is a schematic diagram illustrating the implementation environment of a data processing method according to an exemplary embodiment. For example... Figure 1 As shown, the implementation environment may include at least terminal 01 and server 02. The terminal 01 and server 02 may be directly or indirectly connected through wired or wireless communication, which is not limited in this application.

[0056] Specifically, the terminal 01 can be used to collect data about the environment to be detected. Optionally, the terminal 01 may include, but is not limited to, mobile phones, computers, smart voice interaction devices, smart home appliances, vehicle terminals, aircraft, etc.

[0057] Specifically, server 02 can be used to respond to an environmental detection request, acquire environmental data to be detected collected by terminal 01; perform environmental detection on the environmental data to be detected to obtain a first environmental detection result; acquire the location information of the device that collected the environmental data to be detected if the first environmental detection result meets preset conditions; determine the target grid corresponding to the location information; and send service control instructions to the target device associated with the target grid, so that the target device responds to the service control instructions and performs service operations. Optionally, server 02 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0058] It should be noted that, Figure 1 This is just one example. Other implementation environments may also be included in other scenarios.

[0059] Figure 2 This is a flowchart illustrating a data processing method according to an exemplary embodiment. The method can be used for... Figure 1 In the implementation environment described herein, the steps of the methods described in the embodiments or flowcharts are provided. However, based on conventional or non-inventive labor, more or fewer steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual system or server product execution, the methods shown in the embodiments or drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment). Specifically, as shown in the embodiments or drawings... Figure 2 As shown, the method may include:

[0060] S101. In response to an environmental monitoring request, obtain the environmental data to be monitored.

[0061] Optionally, the environmental data to be detected may include, but is not limited to: weather environmental data, meteorological environmental data, hydrological environmental data, geomorphological environmental data, etc.

[0062] In an optional embodiment, in step S101 above, in response to the environmental detection request, several environmental data points can be randomly extracted from the already collected environmental data to obtain the environmental data to be detected.

[0063] In another alternative embodiment, Figure 3 This is a flowchart illustrating an embodiment of acquiring data about an environment to be detected. For example... Figure 3As shown, in step S101 above, the process of obtaining the environmental data to be detected in response to an environmental detection request may include:

[0064] S1011. In response to the above environmental detection request, determine the detection area whose environmental data meets the preset conditions based on the preset environmental service.

[0065] S1013. Send a data pre-acquisition command to the fourth device that appeared in the above-mentioned detection area during the historical time period.

[0066] S1015. Receive the historical environmental data of the area to be detected returned by the fourth device in response to the data pre-acquisition instruction.

[0067] S1017. Extract a preset number of historical environmental data from the above historical environmental data to obtain the above environmental data to be detected.

[0068] In an optional implementation, in step S1011 above, in response to the environmental detection request, a detection area whose environmental data meets preset conditions (e.g., severe environmental conditions) can be determined from a preset environmental service. Taking weather environmental data as an example, the preset environmental service can be a weather network service, which can identify cities with severe environmental conditions such as rain, snow, fog, hail, and strong winds to obtain the detection area.

[0069] In an optional implementation, in step S1013 above, a small amount of pre-collection orders, i.e., a small amount of data pre-collection instructions, can be sent to a fourth device that appeared in the area to be detected within a historical time period prior to the current time (e.g., within 10 minutes prior to the current time). Exemplarily, the number of the fourth device is at least one, and it may include, but is not limited to, mobile phones, computers, smart voice interaction devices, smart home appliances, vehicle terminals, aircraft, etc.

[0070] In an optional implementation, in step S1015 above, after receiving the data pre-collection instruction sent by the server, the fourth device can respond to the data pre-collection instruction by collecting environmental data within the area to be detected to obtain historical environmental data. Taking weather environmental data as an example, the fourth device can respond to the data pre-collection instruction by collecting weather environmental data within the area to be detected to obtain historical weather environmental data.

[0071] In an optional implementation, in step S1017 above, a preset number of historical environmental data can be randomly extracted from the historical environmental data to obtain the environmental data to be detected.

[0072] In another optional implementation, in step S1017 above, severe environmental data can be extracted from historical environmental data as the environmental data to be tested. Taking weather data as an example, rainy weather data can be extracted from historical environmental data as the environmental data to be tested.

[0073] In one implementation, the environment detection request can be triggered by the server in real time, and the data of the environment to be detected can be acquired in real time. For example, the server triggers the environment detection request in real time and acquires the data of the environment to be detected in real time.

[0074] In another implementation, the environment detection request can also be triggered periodically by the server, which periodically acquires the environment data to be detected. For example, the server triggers the environment detection request in real time and acquires the environment data to be detected periodically.

[0075] The environmental data to be detected in this embodiment is extracted from historical environmental data. This historical environmental data is obtained by collecting environmental data within the detection area from a fourth device that appears within a historical time period and whose environmental data meets preset conditions. The environmental data to be detected is the actual environmental data within the detection area where the environmental data meets the preset conditions, rather than data obtained from a preset environmental service. This makes the acquisition of the environmental data more authentic and reliable. Using this authentic and reliable environmental data can improve the accuracy of subsequent business task scheduling. Furthermore, since the environmental data to be detected is the actual environmental data within the detection area where the environmental data meets the preset conditions, there is no environmental data update delay, which can improve the efficiency of subsequent business task scheduling.

[0076] S103. Perform environmental testing on the above-mentioned environmental data to be tested to obtain the first environmental testing result of the above-mentioned environmental data to be tested.

[0077] Taking weather data as an example, rainy weather can cause reflections and obstructions in photos taken by devices (e.g., car dashcams); fog and snow can cause vehicles to be covered in snow, and roads covered in snow can prevent the capture of road information. Therefore, environmental detection of the data can identify weather conditions such as rain, snow, and fog (e.g., collected images) and assign corresponding environmental labels to this data.

[0078] In an optional embodiment, an environment detection network can be pre-trained, and the environment data to be detected can be input into the environment detection network to output a first environment detection result of the environment data to be detected.

[0079] In another alternative embodiment, Figure 4This is a flowchart illustrating an environmental detection process based on an exemplary embodiment, in which environmental detection is performed on environmental data to be detected to obtain a first environmental detection result for the environmental data to be detected. Figure 4 As shown, in step S103 above, the environmental detection of the environmental data to be detected to obtain the first environmental detection result of the environmental data to be detected may include:

[0080] S1031. Input the above-mentioned environmental data to be detected into multiple environmental classification networks to obtain the environmental classification results of the above-mentioned environmental data to be detected belonging to different environmental categories under each environmental classification network.

[0081] S1033. The environmental classification results belonging to different environmental categories mentioned above shall be used as the first environmental detection results mentioned above.

[0082] In one implementation, in step S1031 above, a sample environment database can be pre-established, and corresponding environment labels can be assigned to the sample environment data in the database. The neural network is trained using the labeled sample environment data until the output of the neural network is similar to the environment labels, thus obtaining an environment classification network. For example, this environment classification network can be a binary classification convolutional neural network (CNN).

[0083] Because the sample environment database contains a large amount of sample environment data, different sample environment data can be used to train multiple different environment classification networks.

[0084] Since the environment classification network is trained using sample environment data from a sample environment database, each network can output multiple classification results for a given set of environment data to be detected. Optionally, these multiple classification results can refer to at least two classification results.

[0085] For example, the environmental classification result can be a classification result of "belonging to a certain environmental category" or "not belonging to a certain environmental category", or it can be a probability value of belonging to a certain environmental category.

[0086] In one implementation, in step S1033 above, the environmental classification results of the environmental data to be detected, belonging to different environmental categories under each environmental classification network, can be used as the first environmental detection result. For example, the result of "belonging to a certain environmental category" or "not belonging to a certain environmental category" can be used as the first environmental detection result, or the probability value of belonging to a certain category can be used as the first environmental detection result.

[0087] Taking weather data as an example, the steps S1031-S1033 above will be described as follows:

[0088] First, a sample environment database is established, which includes environmental data such as reflective shading during rainy weather, snow-covered environment data, and foggy and hazy environment data.

[0089] Figure 5 This is a schematic diagram illustrating reflective shading environmental data during rainy weather, according to an exemplary embodiment. For example... Figure 5 As shown, when weather environmental data (e.g., collected images) is obstructed by obstacles within the field of view, and the obstruction is too large, potentially causing road information to be unrecognizable or incompletely identified, the environmental data involved is determined to be rainy weather reflective obstruction environmental data. For example, when images show raindrops, windshield wipers in operation, water accumulation on the ground, or reflective obstruction, these can all be identified as reflective obstruction environmental data.

[0090] Figure 6 This is a schematic diagram illustrating snow cover environment data according to an exemplary embodiment. For example... Figure 6 As shown, because snow covers the road surface or blocks the wind, the device cannot capture clear road information. For example, when environmental data shows abnormal colors, camera overexposure, snowflakes, etc., it can be determined that the environmental data is covered by snow.

[0091] Figure 7 This is a schematic diagram illustrating foggy, blurred environmental data according to an exemplary embodiment. For example... Figure 7 As shown, due to the blurred vision caused by heavy fog, the device was unable to capture clear road information. For example, when environmental data is severely blurred, or there are issues such as stained car windows or window tinting, it can be determined that the environmental data is blurred by heavy fog.

[0092] Based on the above sample environmental data, such as reflective shading environmental data in cloudy and rainy weather, snow-covered environmental data, and foggy and blurry environmental data, a sample environmental database was established. The reflective shading environmental data in cloudy and rainy weather was labeled with the label of "reflective shading in cloudy and rainy weather," the snow-covered environmental data was labeled with the label of "snow-covered environmental data," and the foggy and blurry environmental data was labeled with the label of "foggy and blurry environmental data."

[0093] The neural network is trained using environmental data labeled with environmental tags until it converges, resulting in the environmental classification network. Since the sample environment database contains a large amount of sample environment data, multiple different environmental classification networks can be trained using different sample environment data.

[0094] Since this environmental classification network is trained using sample environmental data from a sample environmental database, each network can output multiple classification results for a given set of environmental data to be tested. These results can be either "belongs to a certain environmental category" or "does not belong to a certain environmental category," for example, "the environmental data to be tested is rainy weather" or "the environmental data to be tested is foggy and blurry." Alternatively, the results can be probability values ​​indicating belonging to a certain category, such as "the probability that the environmental data to be tested is rainy weather is 90%" or "the probability that the environmental data to be tested is foggy and blurry is 50%."

[0095] It should be noted that, in order to improve the classification accuracy of the environmental classification network and thus the accuracy of task scheduling, the sample environmental database is not limited to rainy weather with glare, snow cover, and fog; it can also include conditions such as windows with water droplets or windshield wipers. Accordingly, the trained environmental classification network can identify environmental data containing water droplets and windshield wipers, etc.

[0096] Figure 8 This is a flowchart illustrating another method for performing environmental detection on environmental data to be detected, according to an exemplary embodiment, to obtain a first environmental detection result of the environmental data to be detected, such as... Figure 8 As shown in the embodiments of this application, the environmental data to be detected is classified into a pre-trained environmental classification network, and the environmental classification results of the environmental data to be detected belonging to different environmental categories under each environmental classification network are obtained, thereby obtaining the first environmental detection result, which improves the determination efficiency and accuracy of the first environmental detection result.

[0097] In an optional embodiment, Figure 9 This is a flowchart illustrating a method for predicting real-world weather within a target grid, according to an exemplary embodiment, such as... Figure 9 As shown, the above method may further include a step of predicting the real weather within the target grid. This step of predicting the real weather within the target grid may include:

[0098] S201. Input the above-mentioned environmental data to be detected into multiple environmental classification networks to obtain the environmental classification results of the above-mentioned environmental data to be detected belonging to different environmental categories under each environmental classification network.

[0099] S203. From the environmental classification results belonging to different environmental categories, determine the environmental classification results of the above-mentioned environmental data to be detected belonging to the same environmental category under each environmental classification network.

[0100] S205. Based on the environmental classification results belonging to the same environmental category, determine the environmental prediction results; the environmental prediction results are used to describe the environmental conditions within the target grid.

[0101] In one implementation, in step S201 above, multiple different environmental classification networks can be pre-trained, and the environmental data to be detected can be input into each environmental classification network respectively to obtain the environmental classification results of the environmental data to be detected belonging to different environmental categories under each environmental classification network. The training process of this environmental classification network and the type of environmental classification results are similar to those in step S1031 above, and will not be described again here.

[0102] Taking weather data as the environment data to be detected, and the probability value of belonging to a certain environment category as the environmental classification result, the above step S201 is explained as follows: Environment classification networks A, B, C, D, and E can be pre-trained. Each of these networks can output multiple environmental classification results for a single set of environment data to be detected. For example, inputting the environment data to be detected into environment classification network A yields probabilities such as "probability 1" for rainy weather and "probability 1" for foggy weather. Inputting the environment data to be detected into environment classification network B yields probabilities such as "probability 2" for rainy weather and "probability 2" for foggy weather.

[0103] In one embodiment, in step S203 above, the environmental classification results of the environmental data to be detected belonging to the same environmental category under each environmental classification network can be extracted from the environmental classification results of the environmental data to be detected belonging to different environmental categories under each environmental classification network.

[0104] Continuing with the example above, when the environmental data to be tested is input into environmental classification network A, we can obtain the probability 1 of the environmental data belonging to overcast / rainy weather and the probability 1 of belonging to foggy / vague weather. When the environmental data to be tested is input into environmental classification network B, we can obtain the probability 2 of the environmental data belonging to overcast / rainy weather and the probability 2 of belonging to foggy / vague weather. Therefore, in environmental classification networks A and B, the environmental classification result of the environmental data to be tested belonging to the same environmental category is either overcast / rainy weather (probability 1 and probability 2) or foggy / vague weather (probability 1 and probability 2).

[0105] In one implementation, in step S205 above, a soft voting algorithm can be used to take the average value of the environmental classification results of all environmental classification networks predicting that the environmental data to be detected belongs to a certain environmental category as the standard, and take the environmental category with the highest average value as the final environmental prediction result.

[0106] Continuing with the above example, inputting the environmental data to be tested into environmental classification network A yields a 99% probability that the weather data belongs to overcast / rainy weather and a 1% probability that it belongs to foggy / vague weather, etc.; inputting the environmental data to be tested into environmental classification network B yields a 49% probability that the environmental data belongs to overcast / rainy weather and a 51% probability that it belongs to foggy / vague weather, etc.; inputting the environmental data to be tested into environmental classification network C yields a 40% probability that the environmental data belongs to overcast / rainy weather and a 60% probability that it belongs to foggy / vague weather, etc.; inputting the environmental data to be tested into environmental classification network D yields a 90% probability that the environmental data belongs to overcast / rainy weather and a 10% probability that it belongs to foggy / vague weather, etc.; inputting the environmental data to be tested into environmental classification network E yields a 30% probability that the environmental data belongs to overcast / rainy weather and a 70% probability that it belongs to foggy / vague weather, etc. The average probability that the network to be tested belongs to the rainy weather category is (99%+49%+40%+90%+30%) / 5 = 0.616, and the average probability that the network to be tested belongs to the foggy weather category is (1%+51%+60%+10%+70%) / 5 = 0.384. Therefore, the final environmental prediction result is rainy weather.

[0107] In another implementation, step S205 above can also employ a weighted voting method to label and profile the environmental classification network based on the accuracy of historical voting results. Increasing the voting weight of high-performing environmental classification networks improves the accuracy of the final weather forecast.

[0108] In this embodiment, multiple environmental classification networks are used to classify the environmental data to be detected. Then, based on the environmental classification results of the environmental data to be detected belonging to the same environmental category under each environmental classification network, the environmental prediction result is determined. This method of determining the environmental prediction result comprehensively considers the classification effect of multiple environmental classification networks, effectively avoids the defects of environmental classification bias caused by a single environmental classification network, and improves the prediction accuracy of the environmental prediction result.

[0109] S105. If the first environmental detection result meets the preset conditions, obtain the location information of the device that collects the environmental data to be detected.

[0110] In this embodiment of the application, if the first environmental detection result meets the preset conditions, the server can obtain the location information of the device collecting the environmental data to be detected through a map or HyperText Markup Language 5 (HTML5).

[0111] Optionally, the first environmental detection result can be a classification result of "belonging to a certain environmental category" or "not belonging to a certain environmental category", or it can be a probability value of belonging to a certain environmental category.

[0112] In one implementation, if the first environmental test result is "belongs to a certain environmental category", the first environmental test result can be considered to meet the preset conditions, that is, the environmental data to be tested is unqualified and has environmental defects; if the first environmental test result is "does not belong to a certain environmental category", the first environmental test result can be considered to not meet the preset conditions, that is, the environmental data to be tested is qualified and does not have environmental defects.

[0113] In another implementation, if the probability value of the first environmental detection result belonging to a certain environmental category is greater than a preset probability threshold, the first environmental detection result can be considered to meet the preset conditions, that is, the environmental data to be detected is unqualified and has environmental defects; if the probability value is less than or equal to the preset probability threshold, the first environmental detection result can be considered to not meet the preset conditions, that is, the environmental data to be detected is qualified and does not have environmental defects.

[0114] S107. Determine the target grid corresponding to the above location information.

[0115] In one implementation, in step S107 above, the server can obtain the trajectory information of the device when collecting the environmental data to be detected based on the location information, perform spatial calculations on the trajectory information, divide the spatial calculation results into a buffer, generate a polygon region, and obtain the target mesh.

[0116] Optionally, the buffer distance can be set differently based on environmental conditions. If, after environmental testing, a high proportion of unqualified environmental data is found, a larger buffer distance is set; conversely, a smaller buffer distance is set. By dynamically adjusting the buffer distance, the flexibility and accuracy of service acquisition scheduling can be improved.

[0117] In another implementation, the urban administrative area can be divided into several unit grids according to certain standards in advance, and the grid where the above location information is located can be determined from these several unit grids to obtain the target grid.

[0118] S109. Send a service control command to the target device associated with the target grid, so that the target device responds to the service control command and performs service operations.

[0119] In an optional implementation, the target device is a first device appearing in the target grid within a preset time period, and the service control instruction is a stop collection instruction corresponding to the collection service. In S109, sending the service control instruction to the target device associated with the target grid so that the target device responds to the service control instruction and performs service operations may include:

[0120] Send the stop acquisition command to the first device so that the first device responds to the stop acquisition command and stops acquiring environmental data within the target grid.

[0121] Optionally, in a device crowdsourcing data collection scenario, if the first environmental detection result meets preset conditions, the environmental data to be detected can be considered unqualified, indicating a severe environmental condition. Therefore, it can be assumed that a severe environmental condition has occurred in the target grid. In this case, a stop-collection command can be sent to the first device appearing in the target grid within a preset time period, preventing the first device from collecting environmental data within the target grid during that time period. This avoids unqualified collection results and unusable business operations caused by the first device continuing to collect environmental data within the target grid during the preset time period. (For example, in map road data production, continuously collecting environmental data from areas with environmental defects may result in images that cannot identify road information, rendering the images unusable for map road data production. Similarly, in traffic congestion prediction, continuously collecting environmental data from areas with environmental defects may result in images that cannot identify congested road sections and congestion information due to fog, rendering the images unusable for traffic congestion prediction.) This approach suffers from significant system resource consumption and high costs. As can be seen, the embodiments of this application can make full use of the coverage of the equipment, transmit the real local weather conditions in a crowdsourcing mode, quickly and accurately adjust the collection strategy, and ensure the collection quality. On the one hand, it can automatically avoid collecting unqualified environmental data to be tested, thereby improving the quality of the collected environmental data to be tested. On the other hand, it can effectively reduce the consumption of system resources and save costs.

[0122] In an optional embodiment, Figure 10 This is a flowchart illustrating a method for determining a target mesh corresponding to location information according to an exemplary embodiment, such as... Figure 10 As shown, after determining the target grid corresponding to the location information, the method may further include:

[0123] S301. Obtain the duration of the preset environment information corresponding to the above location information from the preset environment service.

[0124] S303. Based on the aforementioned duration and the acquisition time of the aforementioned environmental data to be detected, determine the aforementioned preset time period; the start time of the aforementioned preset time period is the aforementioned acquisition time, and the end time of the aforementioned preset time period is the end time of the aforementioned duration.

[0125] S305. Set the state of the target grid during the preset time period to a preset state; the preset state is used to instruct the devices appearing in the target grid to collect environmental data within the target grid.

[0126] In this embodiment of the application, after determining the target grid, a dynamic management period T1 (i.e., a preset time period) for the target grid can also be set. In an optional implementation, in the above step S301, the preset environment information corresponding to the location information and the duration corresponding to the preset environment information can be obtained from the preset environment service, and the collection time of the above-mentioned environment data to be detected can be used as the start time of the preset time period, and the end time of the duration can be used as the end time of the preset time period.

[0127] In an optional implementation, in step S305 above, an environmental defect grid library can also be established. Within a preset time period, the target grid and its related attribute information (including but not limited to dynamic management cycle, creation time, grid border, etc.) are stored in the environmental defect grid library. At this time, the state of the target grid within the preset time period is set to a preset state. This preset state is used to instruct devices appearing in the target grid to collect environmental data within the target grid. For example, the environmental defect grid library may include, but is not limited to, a database or a remote dictionary service (Redis).

[0128] The following explanation uses weather data as an example to illustrate steps S301-S305:

[0129] The city is determined based on location information. The corresponding preset weather information and its duration are determined by retrieving hourly broadcasts from a preset weather network service. For example, if the preset weather information in the weather network service is heavy fog, and the hourly broadcasts determine the fog duration to be 9:00-11:00, and the environmental data to be collected is at 8:30, then the preset time period is 8:30-11:00. The target grid and its related attribute information are stored in an environmental defect grid library, and the status of the target grid from 8:30-11:00 is updated to a preset status. This preset status instructs devices appearing in the target grid to collect environmental data within that grid.

[0130] The embodiments of this application determine a preset time period based on the collection time of the environmental data to be detected and the duration of the preset environmental information obtained from the preset environmental service, that is, determine the dynamic management cycle of the target grid, which can improve the accuracy of determining the dynamic management cycle of the target grid, thereby improving the accuracy of device crowdsourcing collection and scheduling.

[0131] In an optional embodiment, Figure 11 This is a flowchart illustrating, according to an exemplary embodiment, the updating of the state of a target mesh from a preset state to a non-preset state, such as... Figure 11 As shown, the above method may further include:

[0132] S401. When the duration of the preset time period is greater than the preset time threshold and the current time reaches the preset time point within the preset time period, a first data acquisition command is sent to the first device at a preset frequency so that the first device responds to the first data acquisition command and acquires environmental data of the target grid; the duration between the preset time point and the end time of the preset time period is equal to the time difference between the duration of the preset time period and the preset time threshold.

[0133] S403. Receive the environmental data returned by the first device.

[0134] S405. Perform environmental detection on the environmental data returned by the first device to obtain a second environmental detection result.

[0135] S407. If the second environmental detection result does not meet the preset conditions, the state of the target grid is updated from the preset state to a non-preset state; the non-preset state is used to instruct the device appearing in the target grid to collect environmental data within the target grid.

[0136] In one feasible implementation, if the preset time period is too long, the environmental conditions within the target network may have improved, but because the target grid is still in a preset state, devices entering the target grid cannot collect the improved environmental data, resulting in the loss of high-quality environmental data. Therefore, if the preset time period exceeds a preset time threshold and the current time reaches a preset time point within the preset time period, the first device entering the target grid can be allowed to collect environmental data. Environmental detection is then performed on the environmental data returned by the first device. If the second environmental detection result does not meet preset conditions, it can be considered that the environmental data within the target network has improved, and the target grid is removed from the environmental defect grid library. At this point, the target grid's state is updated from the preset state to a non-preset state, allowing devices entering the target grid to collect environmental data. This avoids the situation where the environmental conditions within the target network have improved, but the preset time period is too long, preventing the collection of environmental data within the target grid. This facilitates the crowdsourcing collection of rich environmental data, thereby expanding into richer business applications. For example, rich environmental data can serve as a reference for map road data production and can also be used for traffic congestion prediction.

[0137] Optionally, in step S401 above, the duration between the preset time point and the end time of the preset time period is equal to the time difference between the duration of the preset time period and the preset time threshold. For example, if the duration of the preset time period is 30 minutes and the preset time threshold is 20 minutes, then the duration between the preset time point and the end time of the preset time period is 10 minutes. This preset frequency can be determined according to the actual scenario, for example, 10 minutes.

[0138] Optionally, in step S405 above, the method of performing environmental detection on the environmental data returned by the first device is similar to that in steps S1031-S1033 above, and will not be repeated here.

[0139] Optionally, in step S407 above, the type of the second environmental detection result is similar to the type of the first environmental detection result, and the preset conditions in step S407 are similar to the preset conditions in step S105. For details, please refer to the description in step S105, which will not be repeated here.

[0140] In one feasible embodiment, the above method may further include:

[0141] If the second environmental detection result meets the preset conditions, the target grid is maintained in the preset state.

[0142] Optionally, if the second environmental detection result meets the preset conditions, it can be considered that the environmental data within the target grid has not improved. In this case, the target grid will not be deleted from the environmental defect grid library, meaning the target grid will remain in a state where the first device within the target grid is not allowed to collect environmental data. This further avoids the drawbacks of the first device continuing to collect environmental data within the target grid during a preset time period, resulting in unqualified collection results, unusable services, high system resource consumption, and high costs.

[0143] In an optional embodiment, Figure 12 This is a flowchart illustrating, according to an exemplary embodiment, the updating of the state of a target mesh from a preset state to the aforementioned non-preset state, as shown below. Figure 12 As shown, the above method may further include:

[0144] S501. If the current time is after the aforementioned preset time period, update the state of the target grid from the aforementioned preset state to the aforementioned non-preset state.

[0145] S503. Send a second data acquisition command to the second device appearing in the target grid, so that the second device responds to the second data acquisition command and acquires environmental data within the target grid.

[0146] Optionally, in step S501 above, if the dynamic management cycle of the target mesh fails, that is, if the current time is after the preset time period, the target mesh is deleted from the environmental defect mesh library, and the state of the target mesh is updated from the preset state to a non-preset state.

[0147] Optionally, in step S503 above, after the dynamic management cycle of the target grid fails, a second device appearing in the target grid may be allowed to collect environmental data within the target grid.

[0148] In the event that the dynamic management cycle of the target grid fails, this embodiment allows a second device appearing in the target grid to collect environmental data within the target grid. This avoids situations where the dynamic management cycle of the target grid fails, but devices are still not allowed to collect data. It can fully utilize the coverage of the devices, transmit real local weather conditions in a crowdsourcing mode, quickly and accurately adjust the collection strategy, and ensure the quality of the collection.

[0149] In another alternative embodiment, if the device is outside the target grid, a task acquisition command is always sent to the device so that the device responds to the task acquisition command to acquire environmental data.

[0150] In an optional embodiment, the target device is a third device whose navigation route includes the target grid and whose distance from the target grid is greater than a preset distance threshold. The service control instruction is an environmental warning instruction corresponding to the environmental warning service. In S109, sending the service control instruction to the target device associated with the target grid so that the target device responds to the service control instruction and performs service operations may include:

[0151] Send the aforementioned environmental warning command carrying the aforementioned environmental prediction results to the aforementioned third device, so that the aforementioned third device responds to the aforementioned environmental warning command and displays the aforementioned environmental prediction results and / or displays the environmental warning information corresponding to the aforementioned environmental prediction results in the navigation route of the navigation interface.

[0152] Optionally, a navigation route refers to the route taken from one destination to another.

[0153] In one implementation, if the navigation route of a third device needs to pass through the target grid, and the distance between the third device and the target grid is greater than a preset distance threshold (which can also be understood as the third device needing a preset time to travel to the target grid), an environmental warning command carrying the preset environmental results in the target grid can be sent to the third device. In response to the environmental warning command, the third device displays the environmental prediction results at the location corresponding to the target grid in the navigation route on the navigation interface.

[0154] Figure 13 This is a schematic diagram illustrating the display of environmental prediction results in a navigation route on a navigation interface, according to an exemplary embodiment. For example... Figure 13 As shown, taking the environmental prediction result of rainy weather as an example, "rainy weather" and / or "rainy weather icon" can be displayed at the location corresponding to the target grid in the navigation route.

[0155] In another implementation, the third device may also respond to the environmental warning command by not directly displaying the environmental prediction result in the navigation route of the navigation interface, but instead displaying the environmental warning information corresponding to the environmental prediction result.

[0156] Figure 14 This is a schematic diagram illustrating, according to an exemplary embodiment, how environmental warning information corresponding to environmental prediction results is displayed in a navigation route on a navigation interface. For example... Figure 14 As shown, taking a scenario where the weather forecast indicates cloudy or rainy weather, the navigation route can display a message such as "The weather at your current distance of xx kilometers is cloudy or rainy. Please drive carefully!"

[0157] In this embodiment, after predicting the environmental forecast results in the target grid, an environmental warning command carrying the environmental forecast results can be sent to a third device whose navigation route includes the target grid but has not yet reached it. That is, the environmental forecast results are sent to the navigation route passing through the target grid, so that the third device's account can know the environmental conditions ahead in advance, change the navigation route in advance, and accurately avoid entering the section with a harsh environment, thereby improving the service scheduling accuracy and user experience of the environmental warning service. In addition, since the environmental forecast results can be sent to the third device before it reaches the target grid, the service scheduling efficiency of the environmental warning service can be improved.

[0158] Figure 15 This is a flowchart illustrating another data processing method according to an exemplary embodiment. For example... Figure 15 As shown, this method is applied to a device whose navigation route includes a target grid, and the distance between the device and the target grid is greater than a preset distance threshold. The method includes:

[0159] S601. When the environmental detection result of the environmental data to be detected meets the preset conditions, receive an environmental early warning command carrying the environmental prediction result corresponding to the target grid; the environmental prediction result is determined based on the environmental detection result, the environmental detection result is obtained by performing environmental detection on the environmental data to be detected, the environmental data to be detected is data obtained in response to the environmental detection request, and the target grid is determined based on the location information of the device that collects the environmental data to be detected.

[0160] S603. In response to an environmental warning command, display the above-mentioned environmental prediction results and / or display the environmental warning information corresponding to the environmental prediction results in the navigation route of the navigation interface.

[0161] Optionally, in step S601 above, the process of determining the target grid is the same as in step S107, the process of determining the environmental data to be detected is the same as in step S101, the process of determining the environmental detection results is the same as in step S103, and the process of determining the environmental prediction results is the same as in steps S201-S205, and will not be repeated here. In step S603 above, the process of displaying the environmental prediction results or the corresponding environmental warning information in the navigation route of the navigation interface can be referred to... Figure 8 and Figure 9 This will not be elaborated upon here.

[0162] In this embodiment, after predicting the environmental forecast results in the target grid, an environmental warning instruction carrying the aforementioned environmental forecast results can be sent to a third device whose navigation route includes the target grid but has not yet reached it. That is, the environmental forecast results are sent to the navigation route passing through the target grid, so that the third device's account can know the environmental conditions ahead in advance, change the navigation route in advance, and accurately avoid entering the section with a harsh environment, thereby improving the service scheduling accuracy and user experience of the environmental warning service. In addition, since the environmental forecast results can be sent to the third device before it reaches the target grid, the service scheduling efficiency of the environmental warning service can be improved.

[0163] In an optional embodiment, Figure 16 This is a schematic diagram illustrating an updated navigation route and showing the updated navigation route according to an exemplary embodiment, such as... Figure 16 As shown, after displaying the environmental prediction results and / or the corresponding environmental warning information in the navigation route of the navigation interface in response to the environmental warning command, the above method may further include:

[0164] S701. In response to the navigation route update command triggered based on the above navigation interface, display the navigation route update interface.

[0165] S703. In response to an operation command triggered by the navigation route update interface, update the navigation route based on the operation result corresponding to the operation command to obtain an updated navigation route; the updated navigation route does not include the target grid.

[0166] S705. Display the updated navigation route in the above navigation interface.

[0167] Optionally, a route update control can be set in the navigation interface. The account corresponding to the device can click the update control to trigger the navigation route update command. The device responds to the navigation route update command and displays the navigation route update interface. The account corresponding to the device can perform operations such as input and selection in the navigation route update interface, thereby triggering the operation command. The device responds to the operation command and updates the navigation route based on the operation result corresponding to the operation command, obtaining the updated navigation route, and displays the updated navigation route in the navigation interface.

[0168] In this embodiment, since the updated navigation route does not include the target grid, after the updated navigation route is displayed in the navigation interface, the device can drive according to the updated navigation route, accurately avoiding the target grid with harsh environment, thereby improving the service scheduling accuracy and user experience of the environmental early warning service.

[0169] Figure 17This is a block diagram of a data processing apparatus according to an exemplary embodiment. Figure 17 As shown, the device may include at least:

[0170] The environment data acquisition module 801 is used to acquire the environment data to be detected in response to the environment detection request.

[0171] The first environmental detection result determination module 803 is used to perform environmental detection on the above-mentioned environmental data to be detected, and obtain the first environmental detection result of the above-mentioned environmental data to be detected.

[0172] The location information acquisition module 805 is used to acquire the location information of the device that collects the environmental data to be detected when the first environmental detection result meets the preset conditions.

[0173] The target grid determination module 807 is used to determine the target grid corresponding to the above location information.

[0174] The service control instruction sending module 809 is used to send service control instructions to the target device associated with the target mesh, so that the target device responds to the service control instructions and performs service operations.

[0175] In an optional embodiment, the target device is a first device that appears in the target grid within a preset time period, the service control instruction is a stop collection instruction corresponding to the collection service, and the service control instruction sending module is used to send the stop collection instruction to the first device so that the first device responds to the stop collection instruction and stops collecting environmental data in the target grid.

[0176] In an optional embodiment, after determining the target grid corresponding to the location information, the apparatus further includes:

[0177] The duration determination module is used to obtain the duration of the preset environment information corresponding to the above location information from the preset environment service.

[0178] The preset time period determination module is used to determine the preset time period based on the duration and the collection time of the environmental data to be detected; the start time of the preset time period is the collection time, and the end time of the preset time period is the end time of the duration.

[0179] The status setting module is used to set the status of the target grid during the preset time period to a preset status; the preset status is used to instruct the devices appearing in the target grid to collect environmental data within the target grid.

[0180] In an optional embodiment, the above-described apparatus further includes:

[0181] The first data acquisition instruction sending module is used to send a first data acquisition instruction to the first device at a preset frequency when the duration of the preset time period is greater than a preset time threshold and the current time reaches a preset time point within the preset time period, so that the first device responds to the first data acquisition instruction and acquires environmental data of the target grid; the duration between the preset time point and the end time of the preset time period is equal to the time difference between the duration of the preset time period and the preset time threshold.

[0182] The environmental data receiving module is used to receive environmental data returned by the first device mentioned above.

[0183] The second environmental detection module is used to perform environmental detection on the environmental data returned by the first device to obtain the second environmental detection result.

[0184] The first state update module is used to update the state of the target grid from the preset state to a non-preset state when the second environmental detection result does not meet the preset conditions; the non-preset state is used to instruct the device appearing in the target grid to collect environmental data within the target grid.

[0185] In an optional embodiment, the above-described apparatus further includes:

[0186] The state maintenance module is used to maintain the state of the target grid as the preset state when the second environmental detection result meets the preset conditions.

[0187] In an optional embodiment, the above-described apparatus further includes:

[0188] The second state update module is used to update the state of the target grid from the preset state to the non-preset state when the current time is after the preset time period.

[0189] The second data acquisition command sending module sends a second data acquisition command to the second device appearing in the target grid, so that the second device responds to the second data acquisition command and acquires environmental data within the target grid.

[0190] In an optional embodiment, the first environmental detection result determination module includes:

[0191] The input unit is used to input the above-mentioned environmental data to be detected into multiple environmental classification networks to obtain the environmental classification results of the above-mentioned environmental data to be detected belonging to different environmental categories under each environmental classification network.

[0192] The first environmental detection result determination unit is used to take the environmental classification results belonging to different environmental categories as the first environmental detection result.

[0193] In an optional embodiment, the above-described apparatus further includes:

[0194] The first environmental classification result determination module is used to input the above-mentioned environmental data to be detected into multiple environmental classification networks to obtain the environmental classification results of the above-mentioned environmental data to be detected belonging to different environmental categories under each environmental classification network.

[0195] The second environmental classification result determination module is used to determine, from the environmental classification results belonging to different environmental categories, the environmental classification result of the environmental data to be detected belonging to the same environmental category under each environmental classification network;

[0196] An environmental prediction result determination module is used to determine an environmental prediction result based on the environmental classification results belonging to the same environmental category; the environmental prediction result is used to describe the environmental conditions within the target grid. In an optional embodiment, the target device is a third device whose navigation route includes the target grid and whose distance from the target grid is greater than a preset distance threshold; the service control instruction is an environmental warning instruction corresponding to the environmental warning service; and the service control instruction sending module is used to send the environmental warning instruction carrying the environmental prediction result to the third device, so that the third device responds to the environmental warning instruction and displays the environmental prediction result and / or displays the environmental warning information corresponding to the environmental prediction result in the navigation route of the navigation interface.

[0197] In an optional embodiment, the above-mentioned environmental data acquisition module includes:

[0198] The area to be detected unit is used to respond to the above-mentioned environmental detection request and determine the area to be detected whose environmental data meets the preset conditions based on the preset environmental service.

[0199] The data pre-acquisition instruction sending unit is used to send a data pre-acquisition instruction to the fourth device that appears in the above-mentioned detection area during the historical time period.

[0200] The historical environmental data receiving unit is used to receive the historical environmental data of the area to be detected, which was collected in response to the data pre-collection command and returned by the fourth device.

[0201] The environment data determination unit is used to extract a preset number of historical environment data from the aforementioned historical environment data to obtain the aforementioned environment data to be detected.

[0202] Figure 18This is a block diagram of a data processing apparatus according to an exemplary embodiment. Figure 18 As shown, the device, which is applied to a navigation route that includes a target grid and where the distance between the device and the target grid is greater than a preset distance threshold, may include at least:

[0203] The environmental early warning instruction receiving module 901 is used to receive an environmental early warning instruction carrying the environmental prediction result corresponding to the target grid when the environmental detection result of the environmental data to be detected meets the preset conditions; the environmental prediction result is determined based on the environmental detection result, the environmental detection result is obtained by performing environmental detection on the environmental data to be detected, the environmental data to be detected is data obtained in response to an environmental detection request, and the target grid is determined based on the location information of the device that collects the environmental data to be detected.

[0204] The display module 903 is used to respond to the above-mentioned environmental warning command and display the above-mentioned environmental prediction results and / or the environmental warning information corresponding to the environmental prediction results in the navigation route of the navigation interface.

[0205] In an optional embodiment, after displaying the environmental prediction results and / or the corresponding environmental warning information in the navigation route of the navigation interface in response to the environmental warning command, the device may further include:

[0206] The update interface display module is used to respond to the navigation route update command triggered based on the above navigation interface and display the navigation route update interface.

[0207] The update module is used to respond to the operation command triggered by the navigation route update interface, update the navigation route based on the operation result corresponding to the operation command, and obtain the updated navigation route; the updated navigation route does not include the target grid.

[0208] The navigation route display module is used to display the updated navigation route in the navigation interface.

[0209] It should be noted that the device embodiments provided in this application are based on the same inventive concept as the method embodiments described above.

[0210] This application also provides an electronic device for data processing, which includes a processor and a memory. The memory stores at least one instruction or at least one program. The processor loads and executes the at least one instruction or at least one program to implement the data processing method provided in any of the above embodiments.

[0211] Embodiments of this application also provide a computer-readable storage medium that can be disposed in a terminal to store at least one instruction or at least one program for implementing a data processing method in the method embodiments, wherein the at least one instruction or at least one program is loaded and executed by a processor to implement the data processing method provided in the above method embodiments.

[0212] Optionally, in the embodiments of this specification, the storage medium may be located at at least one of the multiple network servers in a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0213] The memory described in this specification can be used to store software programs and modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system, applications required for functions, etc.; the data storage area may store data created based on the use of the device, etc. Furthermore, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory may also include a memory controller to provide the processor with access to the memory.

[0214] This application also provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the data processing method provided in the above-described method embodiments.

[0215] The data processing method embodiments provided in this application can be executed on a terminal, computer terminal, server, or similar computing device. Taking running on a server as an example, Figure 19 This is a hardware structure block diagram of a data processing server provided according to an exemplary embodiment. For example... Figure 19As shown, the server 1000 can vary significantly due to different configurations or performance. It may include one or more Central Processing Units (CPUs) 1010 (CPUs 1010 may include, but are not limited to, microprocessors (MCUs) or programmable logic devices (FPGAs), a memory 1030 for storing data, and one or more storage media 1020 (e.g., one or more mass storage devices) for storing application programs 1023 or data 1022. The memory 1030 and storage media 1020 may be temporary or persistent storage. The program stored in the storage media 1020 may include one or more modules, each module may include a series of instruction operations on the server. Furthermore, the CPU 1010 may be configured to communicate with the storage media 1020 and execute the series of instruction operations in the storage media 1020 on the server 1000. Server 1000 may also include one or more power supplies 1060, one or more wired or wireless network interfaces 1050, one or more input / output interfaces 1040, and / or one or more operating systems 1021, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0216] The input / output interface 1040 can be used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of server 1000. In one example, the input / output interface 1040 includes a network interface controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the input / output interface 1040 may be a radio frequency (RF) module for wireless communication with the Internet.

[0217] Those skilled in the art will understand that Figure 19 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, server 1000 may also include... Figure 19 The more or fewer components shown, or having the same Figure 19 The different configurations shown.

[0218] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0219] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and server embodiments are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0220] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware, or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0221] The above are merely preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A data processing method, characterized in that, The method includes: In response to an environmental monitoring request, the system acquires environmental data to be monitored. This environmental data is extracted from historical environmental data. The historical environmental data is obtained by collecting environmental data within the monitored area from a fourth device that appeared within the monitored area during a historical time period. The environmental data collected by the fourth device is in the form of images. The monitored area is an area whose environmental data meets preset conditions, as determined from a preset environmental service. Environmental detection is performed on the environmental data to be detected to obtain a first environmental detection result for the environmental data to be detected. If the first environmental detection result meets the preset conditions, obtain the location information of the device that collects the environmental data to be detected; Based on the location information, the trajectory information of the fourth device when collecting the environmental data to be detected is obtained. Spatial calculations are performed on the trajectory information, and the spatial calculation results are divided into buffer zones to generate polygonal regions and obtain the target grid. The distance of the buffer zone is positively correlated with the proportion of non-compliance in the environmental data to be detected. A stop-collection command is sent to a first device that appears in the target grid within a preset time period, so that the first device responds to the stop-collection command and stops collecting environmental data within the target grid.

2. The method according to claim 1, characterized in that, After obtaining the target mesh, the method further includes: Obtain the duration of the preset environment information corresponding to the location information from the preset environment service; The preset time period is determined based on the duration and the collection time of the environmental data to be detected; the start time of the preset time period is the collection time, and the end time of the preset time period is the end time of the duration. The state of the target grid during the preset time period is set to a preset state; the preset state is used to instruct devices appearing in the target grid to stop collecting environmental data within the target grid.

3. The method according to claim 2, characterized in that, The method further includes: If the duration of the preset time period is greater than a preset time threshold, and the current time reaches a preset time point within the preset time period, a first data acquisition command is sent to the first device at a preset frequency, so that the first device responds to the first data acquisition command and acquires environmental data of the target grid; the duration between the preset time point and the end time of the preset time period is equal to the time difference between the duration of the preset time period and the preset time threshold. Receive environmental data returned by the first device; Environmental detection is performed on the environmental data returned by the first device to obtain a second environmental detection result; If the second environmental detection result does not meet the preset conditions, the state of the target grid is updated from the preset state to a non-preset state; the non-preset state is used to instruct the devices appearing in the target grid to collect environmental data within the target grid.

4. The method according to claim 3, characterized in that, The method further includes: If the second environmental detection result meets the preset conditions, the target grid is maintained in the preset state.

5. The method according to claim 3, characterized in that, The method further includes: If the current time is after the preset time period, the state of the target grid will be updated from the preset state to the non-preset state; A second data acquisition command is sent to a second device appearing in the target grid, so that the second device responds to the second data acquisition command and acquires environmental data within the target grid.

6. The method according to any one of claims 1 to 5, characterized in that, The step of performing environmental detection on the environmental data to be detected to obtain a first environmental detection result for the environmental data to be detected includes: The environmental data to be detected is input into multiple environmental classification networks to obtain the environmental classification results of the environmental data to be detected belonging to different environmental categories under each environmental classification network. The environmental classification results belonging to different environmental categories are used as the first environmental detection results.

7. The method according to claim 1, characterized in that, The method further includes: The environmental data to be detected is input into multiple environmental classification networks to obtain the environmental classification results of the environmental data to be detected belonging to different environmental categories under each environmental classification network. From the environmental classification results belonging to different environmental categories, determine the environmental classification results of the environmental data to be detected belonging to the same environmental category under each environmental classification network; Based on the environmental classification results belonging to the same environmental category, an environmental prediction result is determined; the environmental prediction result is used to describe the environmental conditions within the target grid.

8. The method according to claim 7, characterized in that, The method further includes: An environmental warning instruction carrying the environmental prediction result is sent to a third device whose navigation route includes the target grid and whose distance from the target grid is greater than a preset distance threshold. The third device responds to the environmental warning instruction by displaying the environmental prediction result and / or displaying the environmental warning information corresponding to the environmental prediction result in the navigation route on the navigation interface. The environmental warning instruction corresponds to the environmental warning service.

9. The method according to any one of claims 1 to 5, characterized in that, The step of responding to an environmental detection request and acquiring the environmental data to be detected includes: In response to the environmental detection request, the detection area whose environmental data meets the preset conditions is determined based on the preset environmental service; Send a data pre-collection command to the fourth device that appeared in the area to be detected during the historical time period; Receive historical environmental data within the area to be detected, collected in response to the data pre-collection instruction, returned by the fourth device; A preset number of historical environmental data points are extracted from the historical environmental data to obtain the environmental data to be detected.

10. A data processing method, characterized in that, The method, applied to a device whose navigation route includes a target grid and whose distance to the target grid is greater than a preset distance threshold, includes: If the environmental detection result of the environmental data to be detected meets preset conditions, an environmental early warning command carrying the environmental prediction result corresponding to the target grid is received. The environmental prediction result is determined based on the environmental detection result, which is obtained by performing environmental detection on the environmental data to be detected. The environmental data to be detected is data obtained in response to an environmental detection request and is extracted from historical environmental data. The historical environmental data is obtained by collecting environmental data within the area to be detected from devices that appeared in the area to be detected within a historical time period. The collected environmental data is an image. The area to be detected is an area where the environmental data meets preset conditions, determined from a preset environmental service. The target grid is obtained by obtaining the trajectory information of the device collecting the environmental data to be detected based on its location information, performing spatial calculations on the trajectory information, and dividing the spatial calculation result into a buffer zone to generate a polygonal region. The distance of the buffer zone is positively correlated with the non-compliance ratio of the environmental data to be detected. In response to the environmental warning command, the environmental prediction results and / or the corresponding environmental warning information are displayed in the navigation route on the navigation interface.

11. The method according to claim 10, after displaying the environmental prediction result and / or the environmental warning information corresponding to the environmental prediction result in the navigation route of the navigation interface in response to the environmental warning command, the method further includes: In response to a navigation route update command triggered by the navigation interface, a navigation route update interface is displayed; In response to an operation command triggered by the navigation route update interface, the navigation route is updated based on the operation result corresponding to the operation command to obtain the updated navigation route; The updated navigation route does not include the target grid; The updated navigation route is displayed in the navigation interface.

12. A data processing apparatus, characterized in that, The device includes: The environment data acquisition module is used to acquire environment data to be detected in response to an environment detection request. The environment data to be detected is extracted from historical environment data. The historical environment data is obtained by collecting environmental data in the area to be detected from a fourth device that appears in the area to be detected within a historical time period. The environmental data collected by the fourth device is an image. The area to be detected is an area whose environmental data meets preset conditions, as determined from a preset environment service. The first environmental detection result determination module is used to perform environmental detection on the environmental data to be detected and obtain the first environmental detection result of the environmental data to be detected. The location information acquisition module is used to acquire the location information of the device that collects the environmental data to be detected when the first environmental detection result meets the preset conditions. The target grid determination module is used to obtain the trajectory information of the fourth device when collecting the environmental data to be detected based on the location information, perform spatial calculations on the trajectory information, divide the spatial calculation results into buffer zones, generate polygonal regions, and obtain the target grid; the distance of the buffer zone is positively correlated with the non-compliance ratio of the environmental data to be detected. The service control instruction sending module is used to send service control instructions to the target device associated with the target mesh, so that the target device responds to the service control instructions and performs service operations. The service control instruction sending module is further configured to: send a stop collection instruction to a first device that appears in the target grid within a preset time period, so that the first device responds to the stop collection instruction and stops collecting environmental data within the target grid.

13. The apparatus according to claim 12, characterized in that, The device further includes: The duration determination module is used to obtain the duration of the preset environment information corresponding to the location information from the preset environment service; A preset time period determination module is used to determine the preset time period based on the duration and the collection time of the environmental data to be detected; the start time of the preset time period is the collection time, and the end time of the preset time period is the end time of the duration. A status setting module is used to set the status of the target grid to a preset status during a preset time period; the preset status is used to instruct devices appearing in the target grid to stop collecting environmental data within the target grid.

14. The apparatus according to claim 13, characterized in that, The device further includes: The first data acquisition instruction sending module is used to send a first data acquisition instruction to the first device at a preset frequency when the duration of the preset time period is greater than a preset time threshold and the current time reaches a preset time point within the preset time period, so that the first device responds to the first data acquisition instruction and collects environmental data of the target grid; the duration between the preset time point and the end time of the preset time period is equal to the time difference between the duration of the preset time period and the preset time threshold. An environmental data receiving module is used to receive environmental data returned by the first device; The second environmental detection module is used to perform environmental detection on the environmental data returned by the first device and obtain the second environmental detection result. The first state update module is used to update the state of the target grid from the preset state to a non-preset state when the second environmental detection result does not meet the preset conditions; the non-preset state is used to instruct the devices appearing in the target grid to collect environmental data within the target grid.

15. The apparatus according to claim 14, characterized in that, The device further includes: The state maintenance module is used to maintain the state of the target grid as the preset state when the second environmental detection result meets the preset conditions.

16. The apparatus according to claim 14, characterized in that, The device further includes: The second state update module is used to update the state of the target grid from the preset state to the non-preset state when the current time is after the preset time period. The second data acquisition command sending module is used to send a second data acquisition command to a second device appearing in the target grid, so that the second device responds to the second data acquisition command and acquires environmental data within the target grid.

17. The apparatus according to any one of claims 12 to 16, characterized in that, The first environmental detection result determination module includes: The input unit is used to input the environmental data to be detected into multiple environmental classification networks to obtain the environmental classification results of the environmental data to be detected belonging to different environmental categories under each environmental classification network. The first environmental detection result determination unit is used to take the environmental classification results belonging to different environmental categories as the first environmental detection result.

18. The apparatus according to claim 12, characterized in that, The device further includes: The first environmental classification result determination module is used to input the environmental data to be detected into multiple environmental classification networks to obtain the environmental classification result of the environmental data to be detected belonging to different environmental categories under each environmental classification network. The second environmental classification result determination module is used to determine, from the environmental classification results belonging to different environmental categories, the environmental classification result of the environmental data to be detected belonging to the same environmental category under each environmental classification network; The environmental prediction result determination module is used to determine the environmental prediction result based on the environmental classification results belonging to the same environmental category; the environmental prediction result is used to describe the environmental conditions within the target grid.

19. The apparatus according to claim 18, characterized in that, The service control instruction sending module is further configured to: send an environmental warning instruction carrying the environmental prediction result to a third device whose navigation route includes the target grid and whose distance from the target grid is greater than a preset distance threshold, so that the third device responds to the environmental warning instruction and displays the environmental prediction result and / or displays the environmental warning information corresponding to the environmental prediction result in the navigation route of the navigation interface; the environmental warning instruction corresponds to the environmental warning service.

20. The apparatus according to any one of claims 12 to 16, characterized in that, The environmental data acquisition module includes: The area to be detected unit is used to respond to the environmental detection request and determine the area to be detected whose environmental data meets the preset conditions based on the preset environmental service; The data pre-acquisition instruction sending unit is used to send a data pre-acquisition instruction to a fourth device that appears in the area to be detected during a historical time period. A historical environmental data receiving unit is used to receive historical environmental data within the area to be detected, which is collected in response to the data pre-collection instruction and returned by the fourth device. The environment data determination unit is used to extract a preset number of historical environment data from the historical environment data to obtain the environment data to be detected.

21. A data processing apparatus, characterized in that, An apparatus for use in navigation routes that include target grids, where the distance between the device and the target grid is greater than a preset distance threshold, the apparatus comprising: An environmental early warning command receiving module is used to receive an environmental early warning command carrying the environmental prediction result corresponding to the target grid when the environmental detection result of the environmental data to be detected meets preset conditions. The environmental prediction result is determined based on the environmental detection result, which is obtained by performing environmental detection on the environmental data to be detected. The environmental data to be detected is data obtained in response to an environmental detection request and is extracted from historical environmental data. The historical environmental data is obtained by collecting environmental data within the area to be detected from devices that appeared in the area to be detected within a historical time period. The collected environmental data is an image. The area to be detected is an area where the environmental data meets preset conditions, determined from a preset environmental service. The target grid is obtained by obtaining the trajectory information of the device collecting the environmental data to be detected based on its location information, performing spatial calculations on the trajectory information, and dividing the spatial calculation result into a buffer zone to generate a polygonal region. The distance of the buffer zone is positively correlated with the non-compliance ratio of the environmental data to be detected. The display module is used to respond to the environmental warning command by displaying the environmental prediction results and / or the environmental warning information corresponding to the environmental prediction results in the navigation route of the navigation interface.

22. The apparatus according to claim 21, characterized in that, The device further includes: The interface display module is updated to respond to a navigation route update command triggered based on the navigation interface and display the navigation route update interface. The update module is used to respond to an operation command triggered based on the navigation route update interface, update the navigation route based on the operation result corresponding to the operation command, and obtain an updated navigation route; the updated navigation route does not include the target grid; The navigation route display module is used to display the updated navigation route in the navigation interface.

23. An electronic device for a data processing method, characterized in that, The electronic device includes a processor and a memory, the memory storing at least one instruction or at least one program, the at least one instruction or at least one program being loaded and executed by the processor to implement the data processing method as described in any one of claims 1 to 11.

24. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction or at least one program, which is loaded and executed by a processor to implement the data processing method as described in any one of claims 1 to 11.

25. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the data processing method according to any one of claims 1 to 11.

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