Data processing method and device, equipment, medium and program product

Abstract

The invention provides a data processing method and device, equipment, a storage medium and a program product, and can be applied to the technical field of artificial intelligence. The data processing method comprises the following steps: acquiring an environment image of a target area; wherein the environment image is determined based on the shooting data passing authenticity verification; identifying the environment image based on a pre-training model, and determining the type of the target area and attribute information of a target object in the environment image; wherein the target objects of different types of target areas are different; obtaining resource use information of the target area; wherein the resource use information at least comprises any one of water consumption information and electricity consumption information; determining an evaluation result of the target area according to the attribute information of the target object and the resource use information; wherein the evaluation result is used for reflecting the operation condition of the target area.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and more specifically to a data processing method, device, equipment, medium and program product. Background Art

[0002] For corporate financial management, asset inventory plays a very important role. Among them, real estate inventory as fixed assets is also an important part of the asset inventory in the enterprise, which can help the enterprise formulate better operation strategies.

[0003] The existing real estate inventory method is usually carried out manually offline. Specifically, the inventory executor goes offline to conduct an inventory of the real estate to be inventoried and registers the inventory conclusion. This method is carried out offline throughout the process, and it is impossible to monitor the inventory process in real time, resulting in the authenticity and credibility of the final inventory conclusion cannot be guaranteed. In addition, this method mainly relies on the subjective experience of the inventory executor and lacks standardized evaluation standards. The accuracy of the inventory results is low and it is not conducive to data tracing. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a data processing method, apparatus, device, medium and program product for improving the accuracy of inventory results.

[0005] According to a first aspect of the present disclosure, a data processing method is provided, the method comprising: acquiring an environmental image of a target area; wherein the environmental image is determined based on photographic data that has passed authenticity verification; recognizing the image based on a pre-trained model to determine the type of the target area and attribute information of the target object in the image; wherein the target objects of different types of target areas are different; acquiring resource usage information of the target area; wherein the resource usage information includes at least any one of water usage information and electricity usage information; determining an evaluation result of the target area based on the attribute information of the target object and the resource usage information; wherein the evaluation result is used to reflect the operating conditions of the target area.

[0006] According to an embodiment of the present disclosure, the method also includes verifying the authenticity of the captured data: obtaining parameter information of the captured data; wherein the parameter information includes source information and generation time; based on the source information, determining whether the captured data is sent by a designated device; wherein the designated device is used to perform capturing and transmission operations under a first preset condition; in the case where the captured data is sent by the designated device, determining whether the generation time of the captured data satisfies a second preset condition; in the case where the generation time satisfies the second preset condition, determining an environmental image of the target area based on the captured data.

[0007] According to an embodiment of the present disclosure, the first preset condition is that the distance between the designated device and the target area is less than a first threshold; the second preset condition is that the difference between the upload time of the shooting data and the generation time of the shooting data is less than a second threshold.

[0008] According to an embodiment of the present disclosure, the pre-trained model includes an image classification module and multiple parallel object recognition modules, wherein different object recognition modules recognize different target objects.

[0009] According to an embodiment of the present disclosure, an environmental image is recognized based on a pre-trained model to determine the type of a target area and the attribute information of a target object in the environmental image, including: pre-processing the environmental image; passing the pre-processed image into the pre-trained model, and determining the type of the environmental image based on an image classification module in the pre-trained model; after determining the type of the environmental image, passing the environmental image into a corresponding recognition module, and determining the attribute information of the target object in the environmental image based on the recognition module; wherein the attribute information includes at least any one of quantity information and status information of the target object.

[0010] According to an embodiment of the present disclosure, the operating conditions of a target area are determined based on the attribute information of a target object and the resource usage information, including: determining first information of the target area based on the attribute information of the target object; determining second information of the target area based on the resource usage information of the target object; and determining an evaluation result of the target area based on the first information and the second information; wherein the first information is used to reflect the changing trend of the attribute information of the target object; and the second information is used to reflect the changing trend of the resource usage information.

[0011] According to an embodiment of the present disclosure, determining first information of a target area based on attribute information of a target object includes: acquiring first reference information of the target area; wherein the first reference information includes historical attribute information of the target object in the target area and / or attribute information of target objects in areas of the same type; and determining the first information of the target area based on the first reference information and the attribute information.

[0012] According to an embodiment of the present disclosure, determining second information of a target area based on resource usage information of a target object includes: acquiring second reference information of the target area; wherein the second reference information includes historical resource information of the target area and / or resource information of a similar type of area; and determining the second information of the target area based on the second reference information and resource usage.

[0013] A second aspect of the present disclosure provides a data processing device, including: a first acquisition module, used to acquire an environmental image of a target area; wherein the environmental image is determined based on photographed data that has passed authenticity verification; an identification module, used to identify the image based on a pre-trained model, and determine the type of the target area and attribute information of the target object in the image; wherein the target objects of different types of target areas are different; a second acquisition module, used to acquire resource usage information of the target area; wherein the resource usage information includes at least any one of water usage information and electricity usage information; and a determination module, used to determine an evaluation result of the target area based on the attribute information of the target object and the resource usage information; wherein the evaluation result is used to reflect the operating conditions of the target area.

[0014] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0015] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the above computer program or instructions are executed by a processor.

[0016] The fifth aspect of the present disclosure further provides a computer program product, including a computer program or instructions, which implement the steps of the above method when the above computer program or instructions are executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above contents and other purposes, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0018] Figure 1 The application scenario diagram of the data processing method, apparatus, device, medium and program product according to the embodiments of the present disclosure is schematically shown;

[0019] Figure 2 A flowchart schematically shows a data processing method according to an embodiment of the present disclosure;

[0020] Figure 3 A flowchart of verifying the authenticity of an image according to an embodiment of the present disclosure is schematically shown;

[0021] Figure 4 A flowchart schematically shows how to identify an environmental image based on a pre-trained model and determine the type of a target area and attribute information of a target object in the environmental image according to an embodiment of the present disclosure;

[0022] Figure 5A flowchart schematically illustrates determining the business situation of a target area based on the attribute information and resource usage information of a target object according to an embodiment of the present disclosure;

[0023] Figure 6 A structural block diagram schematically shows a data processing device according to an embodiment of the present disclosure; and

[0024] Figure 7 A block diagram of an electronic device suitable for implementing a data processing method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0025] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0026] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "include", "comprising", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0027] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0028] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0029] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0030] In the technical solution of the present disclosure, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0031] In the scenario of using personal information for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure provide users with corresponding operation portals for users to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating a person's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs, and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge and skills, and have reached a certain level of professionalism.

[0032] An embodiment of the present disclosure provides a data processing method, including: receiving an image of a target area; wherein the image is a photographed image that has passed authenticity verification; identifying the image based on a pre-trained model to determine the type of the target area and attribute information of the target object in the image; wherein the target objects of different types of target areas are different; obtaining resource usage information of the target area; wherein the resource usage information includes at least any one of water usage information and electricity usage information; determining the operating conditions of the target area based on the attribute information of the target object and the resource usage information; wherein the evaluation result is used to reflect the operating conditions of the target area.

[0033] Figure 1 The application scenario diagram of the data processing method, apparatus, device, medium and program product according to the embodiments of the present disclosure is schematically shown.

[0034] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0035] The user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0036] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0037] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0038] It should be noted that the data processing method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the data processing device provided in the embodiment of the present disclosure can generally be set in the server 105. The data processing method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the data processing device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0039] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0040] The following will be based on Figure 1 The scene described by Figure 2~Figure 5 The data processing method of the disclosed embodiment is described in detail.

[0041] Figure 2 The flowchart of the data processing method according to the embodiment of the present disclosure is schematically shown.

[0042] like Figure 2 As shown, the data processing method of this embodiment includes operations S210 to S240.

[0043] In operation S210, an environment image of a target area is acquired; wherein the environment image is determined based on photographed data that has passed authenticity verification.

[0044] In some embodiments, the target area is a building with a specific purpose, for example, it may include residential properties (such as apartments, villas, houses, etc.), commercial properties (such as shopping malls, office buildings, restaurants, hotels, etc.), industrial properties (such as factories, warehouses, computer rooms, etc.), etc. Receive an environmental image of the target area based on the transmission of the designated device, and the environmental image is used to reflect the business situation of the target area. Among them, the authenticity verification includes verifying the generation location and generation time of the shooting data to ensure that the shooting data is taken at the designated time and the designated location, and can truly reflect the situation of the target area, thereby improving the authenticity of the environmental image.

[0045] The shooting data can be in the form of pictures or videos, for example. Correspondingly, the environment image can be a static photo taken in real time by the designated device, or an image frame captured from a video recorded in real time by the designated device. For example, the current environment is directly captured based on the built-in camera function of the designated device to obtain the environment image. Alternatively, the current environment is recorded continuously for a period of time based on the video recording function of the designated device to obtain a video file of the current environment, and key frames are extracted from the video file as the environment image to perform subsequent steps.

[0046] In operation S220, the environment image is recognized based on the pre-trained model to determine the type of the target area and the attribute information of the target object in the environment image.

[0047] In some embodiments, the environment image is identified by a pre-trained model to determine the type of the target area and the attribute information of the target object in the environment image. The target object is an object that can reflect the use of the target area in the current environment image. Different types of areas correspond to different target objects. The attribute information of the target object is the parameter information of the target object in the image, for example, it may include the quantity information of the target object, the status information of the target object, etc. The type of the target area can be divided into equipment-type rooms, business-type rooms, etc. If the type of the target area is an equipment-type room, the target object in the image is a device. If the type of the target area is a business-type room, the target object in the image is a portrait. If the target object is a portrait, the attribute information of the target object may include, for example, the number of portraits in the environment image. If the target object is a device, the attribute information of the target object may include the number of devices and the status of the device (whether it is running), etc.

[0048] In operation S230, resource usage information of the target area is obtained; wherein the resource usage information includes at least one of water usage information and electricity usage information.

[0049] In some embodiments, in addition to the real-time shooting scene of the target area, the resource usage information of the target area can also be associated based on the pre-registered real estate information of the target area, so as to jointly conduct real estate inventory from multiple dimensional data to improve the accuracy of the real estate inventory.

[0050] In operation S240, an evaluation result of the target area is determined according to the attribute information and resource usage information of the target object; wherein the evaluation result is used to reflect the business status of the target area.

[0051] In some embodiments, the business conditions of the target area are determined based on the attribute information of the target object and the resource usage conditions, and the real estate inventory is completed. Different types of target areas have different corresponding evaluation criteria.

[0052] The data processing method provided by the embodiment of the present disclosure recognizes images through a pre-trained model, extracts attribute information of the target object used to evaluate whether the area is idle, and associates the resource usage information of the target area, so as to jointly determine the operating conditions of the resources to be evaluated from the two dimensions of target object and resource usage, realize the standardization and visualization of fixed asset evaluation, and improve the accuracy and authenticity of the evaluation results.

[0053] exist Figure 2 Based on the above data processing method, the data processing method of this embodiment further includes: in operation S250, authenticity verification is performed on the photographed data.

[0054] In some embodiments, operation S250 may be performed before operation S210 is performed to verify the authenticity of the captured data to ensure that the environment image obtained based on the captured data is a real image captured in real time.

[0055] Figure 3 The flowchart of verifying the authenticity of photographed data according to an embodiment of the present disclosure is schematically shown.

[0056] like Figure 3 As shown, the authenticity verification of the photographed data in this embodiment includes operations S251 to S254.

[0057] In operation S251, parameter information of the photographing data is acquired; wherein the parameter information includes source information and generation time.

[0058] In some embodiments, the authenticity is verified based on the parameter information of the captured image by acquiring the parameter information of the captured data. The source information is used to represent the attribute information of the capturing device, such as the serial number and model of the device, and the generation time is the time when the image was captured. The captured data can be in image format or video format.

[0059] Optionally, if the shooting data uploaded by the designated device is in image format, the parameter information may be Exchangeable image file format (Exif) information. Exif information is the inherent information generated when the photo is taken, including the location and time of shooting, and the unique ID of the shooting device. If the data uploaded by the designated device is in video format, the parameter information may be video metadata. Video metadata is information embedded in the video file, including various detailed information such as the shooting time, location, and device model of the video. For example, the metadata of the video file can be read through relevant software (MediaInfo, ExifTool, etc.) to obtain reference information of the shooting data.

[0060] In operation S252, based on the source information, it is determined whether the captured data is sent by a designated device; wherein the designated device is used to perform the capturing and transmitting operations under a first preset condition.

[0061] In some embodiments, the source information is verified to determine whether the device is sent by a designated device to ensure that the data is actually taken. For example, by obtaining the ID number of the shooting device, it is checked whether the shooting device is in a preset designated device list. If the shooting device is a designated device, it is determined that the source of the shooting data is credible.

[0062] Specifically, the designated device is a device installed with a first component, wherein the first component is used to control the shooting permission and data upload permission of the device, so that the device can perform shooting and transmission operations only when a first preset condition is met. The first preset condition is: the distance between the designated device and the target area does not exceed a first threshold.

[0063] The designated device is also provided with a second component for displaying geographic spatial data. In response to receiving a shooting task, the geographic coordinates of the target area are set as the target location, and the second component obtains the current geographic coordinates of the designated device in real time and calculates the distance information between the current location and the target location.

[0064] The distance information calculated by the second component is sent to the first component in real time, and the first component determines whether the distance information meets the first preset condition. When the distance information meets the first preset condition (that is, when the distance between the current geographic coordinates and the target geographic coordinates does not exceed the first threshold), the shooting function and data upload function of the device are automatically enabled. At this time, the user can shoot and transmit images and / or videos based on the device. When the distance information does not meet the first preset condition, the shooting function and data upload function of the device are disabled, and shooting and uploading operations cannot be performed based on the device. The second component can be a Geographic Information System (GIS) component.

[0065] By verifying the designated device, it can be ensured that the images and / or videos are data actually captured at the target area.

[0066] In operation S253, in a case where the photographed image is transmitted by the designated device, it is determined whether the generation time of the photographed data satisfies a second preset condition.

[0067] In some embodiments, when it is determined that the shooting data is sent by a designated device, the generation time of the shooting data (ie, image and / or video) is verified to ensure that the shooting data is shot in real time.

[0068] Specifically, after obtaining the generation time of the shooting data, the generation time of the shooting data is compared with the time when the shooting data is received, and based on the difference between the reception time of the shooting data and the generation time of the shooting data, it is determined whether the generation time of the image meets the second preset condition. The second preset condition is: the difference between the reception time of the shooting data and the generation time of the shooting data is less than the second threshold. When the difference between the reception time of the shooting data and the generation time of the shooting data is less than the second threshold, the shooting data is real-time shooting, and the shooting data can be further processed and analyzed. When the difference between the reception time of the shooting data and the generation time of the shooting data is greater than the second threshold, it is determined that the shooting data is unreliable and the shooting data is rejected.

[0069] In operation S254, when the generation time satisfies the second preset condition, an environment image of the target area is determined based on the photographing data.

[0070] In some embodiments, when the generation time of the shooting data meets the second preset condition, the shooting data is determined to be data shot on-site and in real time and can reflect the actual situation of the target area, and an environmental image of the target area is obtained based on the shooting data.

[0071] Optionally, if the shooting data is a static photo, the photo is directly determined as the environmental image of the target area. If the shooting data is a recorded video, multiple image frames can be captured from the video and the captured image frames are used as the environmental image of the target area. The above operations S220 to S240 are performed on the environmental image to analyze the use of the target area based on the environmental image, determine the operating conditions of the target area, and complete the inventory of the operating conditions of the target area.

[0072] The disclosed embodiment can effectively improve the authenticity of the environmental image by verifying the authenticity of the received shooting data, ensuring that the picture displayed based on the environmental image is an image generated in real time in the target area at the current time, thereby improving the accuracy of usage evaluation.

[0073] Furthermore, in addition to verifying the shooting location and shooting time of the image based on the parameter information of the shooting data to ensure the authenticity of the shooting data, the picture content of the shooting data can be further analyzed, such as analyzing the light and shadow position and dynamic characteristics in the image, to ensure that the shooting data is shot in real time and has not been modified later, thereby ensuring the authenticity and credibility of the environmental image.

[0074] Static photos have higher resolution and can retain more detailed information, while video image frames have richer dynamic information. For example, the dynamic changes of the current target area, such as the trend of changes in the flow of people, can be analyzed by capturing continuous time series information. Relevant personnel in this field can determine the format of shooting data based on actual application scenarios. For example, the shooting data formats corresponding to different types of target areas can be different. If the target area is an equipment room and its corresponding target object is equipment, static photos can be used as shooting data and the content of the photos can be subsequently analyzed. If the target area is a business room, this type of target area involves changes in the flow of people, then the real-time video can be used as shooting data, and relevant information can be obtained by performing dynamic feature analysis on some image frames in the video. The corresponding shooting form can be selected according to the characteristics of the target area to obtain shooting data in the corresponding format, thereby effectively improving the shooting efficiency and the quality of the environmental image, and meeting the data analysis needs of different areas.

[0075] Figure 4 A flowchart is schematically shown for identifying an environmental image based on a pre-trained model, and determining the type of a target area and attribute information of a target object in the environmental image according to an embodiment of the present disclosure.

[0076] like Figure 4 As shown, this embodiment recognizes the environment image based on the pre-trained model, and determines the type of the target area and the attribute information of the target object in the environment image, including operations S410 to S430.

[0077] In operation S410, the environment image is pre-processed.

[0078] In some embodiments, by preprocessing the image, the pre-trained model can better understand and identify features in the image, thereby improving the accuracy of subsequent image processing.

[0079] Specifically, the preprocessing process includes: normalizing the image, denoising, converting the color space, adjusting the channel order, etc., to improve the recognition efficiency of the pre-trained model.

[0080] Among them, image normalization is used to scale the pixel values ​​of the image to a specific range, thereby accelerating the processing speed of the model. Denoising methods may include, for example, Gaussian blurring, mean filtering, etc. Removing image noise can improve image quality. Color space conversion refers to converting an image from one color space to a color space suitable for the model, such as from RGB to grayscale or HSV, to help the model focus on specific features and improve the accuracy of the features. Channel order adjustment refers to adjusting the input order of the image so that the order of the image channels matches the pre-trained model. For example, some models require the input order to be (channel, height, width), while others are (height, width, channel). Therefore, before entering the model, the order of the input image channels needs to be adjusted to adapt to the model and improve recognition efficiency.

[0081] In operation S420, the preprocessed environment image is passed into the pre-trained model, and the type of the environment image is determined based on the image classification module in the pre-trained model.

[0082] In operation S430, after determining the type of the environment image, the environment image is transmitted to a corresponding recognition module to determine the attribute information of the target object in the environment image; wherein the attribute information includes at least one of the quantity information and the state information of the target object.

[0083] In some embodiments, the pre-trained model includes an image classification module and multiple parallel recognition modules; wherein different recognition modules recognize different target objects.

[0084] During the specific implementation process, the preprocessed image is passed into the pre-trained model. The image classification module in the pre-trained model first determines the type of image to obtain the image classification result, and then the image is passed into the corresponding object recognition module based on the image classification result to identify the target object in the image.

[0085] Specifically, the type of environmental image is determined based on the image classification module in the pre-trained model, including: extracting the feature map of the input image through convolution operations and pooling operations, stretching the feature map of the input image into a one-dimensional feature vector, connecting the fully connected layer, mapping the feature map to the category, obtaining the probability value that the input image belongs to each category, and selecting the category with the largest probability value as the classification result of the input image.

[0086] Among them, the fully connected layer includes multiple neurons, each neuron needs to be connected to all neurons in the previous layer, and the flattened one-dimensional feature vector is weighted summed through the fully connected layer, and processed by the activation function to form the final output. Each neuron is fully connected to all neurons in the previous layer, which can effectively realize the comprehensive integration of features and classification decisions, and improve the accuracy of classification results. Among them, the categories of images can include, for example: commercial, industrial and mixed. Commercial categories include office buildings, shopping malls, restaurants, etc. The operating conditions of such buildings are related to the flow of people. Industrial categories include warehouses, factories, computer rooms, etc. The operating conditions of such buildings are related to the amount of equipment. Mixed categories refer to equipment that takes into account both production and sales, such as buildings that combine warehousing and retail. For such buildings, their operating conditions are related to both the flow of people and the amount of equipment.

[0087] After determining the type of the environmental image, the environmental image is transmitted to a corresponding recognition module, and the attribute information of the target object in the environmental image is determined based on the recognition module, including: receiving an image classification result, transmitting the image to a corresponding recognition module based on the image classification result, and having the recognition module recognize feature information of the target object in the image to obtain the attribute information of the target object.

[0088] Specifically, after receiving the input image, the recognition module recognizes the feature information of the target object in the image based on the object recognition algorithm to obtain the attribute information of the target object, wherein the attribute information of the target object may include, for example, the quantity information and state information of the target object.

[0089] Specifically, the target object in the environmental image is identified by an object recognition algorithm, and the target object is analyzed and counted to obtain the attribute information of the target object. The analysis and statistics of the target object include: obtaining the bounding box coordinates of the target object, counting the number of bounding coordinates, obtaining the quantity information of the target object, and obtaining the target object image based on the bounding coordinate box cropping, and analyzing the target image to obtain the state information of the target object. The identification of the target object in the environmental image by the object recognition algorithm may include, for example, the following steps: performing feature extraction operations and target detection and recognition operations on the environmental image. The object recognition algorithm may include, for example, a first algorithm and a second algorithm, the first algorithm may be a traditional feature extraction method, and the second algorithm may be a deep learning method (such as Yolo algorithm, SSD algorithm, FasterR-CNN algorithm, etc.).

[0090] Identifying the target object in the environment image based on the first algorithm includes: using algorithms such as Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) to detect key points or feature points in the image. Describing the detected feature points and generating feature descriptors, which contain local information of the feature points, such as direction, scale, etc. Using the feature descriptors to perform feature matching, similar feature points in different images are found, thereby achieving preliminary positioning of the target object. Identifying the target object in the environment image based on the second algorithm includes: extracting features from the image through multi-layer convolution and pooling operations to obtain multiple feature maps, wherein these feature maps contain feature information of the image at different levels. After obtaining the feature information in the environment image, a candidate region can be generated based on the feature information, and the candidate region can be classified based on the classifier to determine whether the candidate region contains the target object. For the region containing the target object, the precise position of the target object in the image is further determined to obtain the target object in the environment image.

[0091] The recognition module may include, for example, a first recognition module and a second recognition module, wherein the first recognition module is used to recognize a person in an image, and the second recognition module is used to recognize a device in an image. When the image classification result is a commercial class, the target object in the image is recognized based on the first recognition module. When the image classification result is an industrial class, the target object in the image is recognized based on the second recognition module. When the image classification result is a mixed class, the target object is recognized based on the first recognition module and the second recognition module.

[0092] Taking the target area as an industrial building (specifically a computer room) as an example, after the image of the target area is transmitted to the second recognition module, the second recognition module recognizes the server in the image (at this time, the target object recognized by the image recognition module is the server), and based on the recognition result, the number information of the servers in the image and the status information of the servers (such as whether the server is in working state) are counted to obtain the attribute information of the target object.

[0093] The disclosed embodiment can effectively improve the efficiency and accuracy of object recognition by determining the recognition module for identifying the target object based on the identified image type, and identifying the target object based on the recognition module. Among them, multiple recognition modules are set in parallel, which can effectively improve the recognition efficiency. Through parallel computing, multiple recognition modules can process different tasks at the same time, thereby significantly improving the overall processing speed. And based on the image type, the environmental image is transmitted to the corresponding parallel module, which can effectively improve the recognition accuracy of the target object. Each recognition module is optimized for a specific object type, so it can more accurately identify objects of that type, reduce the possibility of false positives and false negatives, and improve the accuracy of recognition.

[0094] Figure 5 The flowchart of determining the business situation of a target area according to the attribute information and resource usage information of a target object according to an embodiment of the present disclosure is schematically shown.

[0095] like Figure 5 As shown, this embodiment determines the business situation of the target area according to the attribute information of the target object and the resource use information, including operations S510 to S530.

[0096] In operation S510, first information of a target area is determined based on attribute information of a target object.

[0097] In some embodiments, the first information is used to reflect the change trend of the target object attribute information.

[0098] Specifically, operation S510 includes: acquiring first reference information of the target area; and determining first information of the target area based on the first reference information and attribute information, wherein the first reference information includes historical attribute information of the target object in the target area and / or attribute information of the target object in the same area.

[0099] Optionally, the historical attribute information of the target object in the target area is used as the first reference information, and the first information is obtained by comparing the current attribute information of the target object in the target area with the historical attribute information. And / or, the attribute information of the target object in the same region is used as the first reference information, and the first information is obtained by comparing the attribute information of the target object in the same region with the attribute information of the target region. The attribute information of the target object in the same region may be, for example, the average of the attribute information of multiple target objects in the same region.

[0100] In operation S520, second information of the target area is determined based on the resource usage information of the target object.

[0101] In some embodiments, the second information is used to reflect a change trend of the resource usage information.

[0102] Specifically, operation S520 includes: obtaining second reference information of the target area; and determining second information of the target area based on the second reference information and resource usage. The second reference information includes historical resource information of the target area and / or resource usage information of similar areas.

[0103] Optionally, the historical resource information of the target area is used as the second reference information, and the second information is obtained by comparing the current resource usage information of the target area with the historical resource information. And / or, the resource usage information of the same type of area is used as the second reference information, and the second information is obtained by comparing the resource usage information of the same type of area with the resource usage information of the target area. The resource usage information of the same type of area can be, for example, the average of the resource usage information of multiple areas of the same type.

[0104] In operation S530, an evaluation result of the target area is determined based on the first information and the second information.

[0105] In some embodiments, different types of target areas correspond to different evaluation rules, and different evaluation rules calculate the operating conditions of the target areas in different ways, which is specifically manifested in that the weights of the factors in the first information and the second information are different in different evaluation rules. For example, if the target area is a commercial building, the weight of the first information should be higher than the weight of the second information. If the target area is an industrial building, the weight of the first information should be lower than the weight of the second information. By setting different evaluation rules, accurate evaluation of different types of buildings can be achieved, and the accuracy of the inventory of idle buildings can be improved.

[0106] Specifically, the operating conditions of the target area may include, for example, idle or not idle. Among them, not idle may further include: normal operation or idle trend. For areas with idle trend, the evaluation frequency may be increased to timely update the idle condition of the building.

[0107] The data processing method provided by the embodiment of the present disclosure can effectively improve the comprehensiveness and accuracy of the inventory results of the real estate in the target area by jointly determining the evaluation results of the target area based on the first information and the second information, reduce the evaluation errors caused by inaccurate or misleading single information, and improve the accuracy of the evaluation results. In addition, setting corresponding evaluation rules according to the type of the target area can improve the degree of fit between the evaluation rules and the target area, help to more accurately reflect the actual situation of the target area, and effectively improve the pertinence and effectiveness of the evaluation.

[0108] Based on the above data processing method, the present disclosure also provides a data processing device. Figure 6 The device is described in detail.

[0109] Figure 6 The structure block diagram of a data processing device according to an embodiment of the present disclosure is schematically shown.

[0110] like Figure 6 As shown, the data processing device 600 of this embodiment includes a first acquisition module 610 , an identification module 620 , a second acquisition module 630 and a determination module 640 .

[0111] The first acquisition module 610 is used to acquire an environmental image of the target area, wherein the environmental image is determined based on the photographed data that has passed the authenticity verification. In one embodiment, the first acquisition module 610 can be used to perform the operation S210 described above, which will not be repeated here.

[0112] The recognition module 620 is used to recognize the environment image based on the pre-trained model, determine the type of the target area and the attribute information of the target object in the environment image; wherein the target objects of different types of target areas are different. In one embodiment, the recognition module 620 can be used to perform the operation S220 described above, which will not be repeated here.

[0113] The second acquisition module 630 is used to acquire resource usage information of the target area, wherein the resource usage information includes at least one of water usage information and electricity usage information. In one embodiment, the second acquisition module 630 can be used to perform the operation S230 described above, which will not be described in detail here.

[0114] The determination module 640 is used to determine the evaluation result of the target area according to the attribute information and resource usage information of the target object; wherein the evaluation result is used to reflect the business status of the target area. In one embodiment, the determination module 640 can be used to perform the operation S240 described above, which will not be repeated here.

[0115] According to an embodiment of the present disclosure, any multiple modules of the first acquisition module 610, the identification module 620, the second acquisition module 630, and the determination module 640 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the first acquisition module 610, the identification module 620, the second acquisition module 630, and the determination module 640 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, at least one of the first acquisition module 610 , the identification module 620 , the second acquisition module 630 , and the determination module 640 may be at least partially implemented as a computer program module, and when the computer program module is executed, a corresponding function may be performed.

[0116] Figure 7 A block diagram of an electronic device suitable for implementing a data processing method according to an embodiment of the present disclosure is schematically shown.

[0117] like Figure 7 As shown, the electronic device 700 according to an embodiment of the present disclosure includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage part 708 to a random access memory (RAM) 703. The processor 701 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 701 may also include an onboard memory for caching purposes. The processor 701 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0118] In RAM 703, various programs and data required for the operation of electronic device 700 are stored. Processor 701, ROM 702 and RAM 703 are connected to each other via bus 704. Processor 701 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 702 and / or RAM 703. It should be noted that the program can also be stored in one or more memories other than ROM 702 and RAM 703. Processor 701 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0119] According to an embodiment of the present disclosure, the electronic device 700 may further include an input / output (I / O) interface 705, which is also connected to the bus 704. The electronic device 700 may further include one or more of the following components connected to the input / output (I / O) interface 705: an input portion 706 including a keyboard, a mouse, etc.; an output portion 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 708 including a hard disk, etc.; and a communication portion 709 including a network interface card such as a LAN card, a modem, etc. The communication portion 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the input / output (I / O) interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed, so that a computer program read therefrom is installed into the storage portion 708 as needed.

[0120] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0121] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 702 and / or RAM 703 described above and / or one or more memories other than ROM 702 and RAM 703.

[0122] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the data processing method provided by the embodiment of the present disclosure.

[0123] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 701. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0124] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 709, and / or installed from the removable medium 711. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0125] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 709, and / or installed from the removable medium 711. When the computer program is executed by the processor 701, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0126] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0127] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0128] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0129] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A data processing method, characterized in that: The method comprises: Acquire an environmental image of a target area; wherein the environmental image is determined based on photographic data that has passed authenticity verification; Recognize the environment image based on the pre-trained model to determine the type of the target area and the attribute information of the target object in the environment image; wherein the target objects of different types of target areas are different; Acquire resource usage information of the target area; wherein the resource usage information includes at least one of water usage information and electricity usage information; An evaluation result of the target area is determined according to the attribute information of the target object and the resource usage information; wherein the evaluation result is used to reflect the operating status of the target area.

2. The data processing method according to claim 1, characterized in that: The method further includes verifying the authenticity of the photographed data: Acquire parameter information of the shooting data; wherein the parameter information includes source information and generation time; Based on the source information, determining whether the captured data is sent by a designated device; wherein the designated device is used to perform a capture and transmission operation under a first preset condition; In the case where the shooting data is sent by a designated device, determining whether the generation time of the shooting data meets a second preset condition; When the generation time satisfies a second preset condition, an environmental image of the target area is determined based on the photographing data.

3. The data processing method according to claim 2, characterized in that: The first preset condition is that the distance between the designated device and the target area is less than a first threshold; The second preset condition is that a difference between an upload time of the shooting data and a generation time of the shooting data is less than a second threshold.

4. The data processing method according to claim 1, characterized in that: The pre-trained model includes an image classification module and a plurality of parallel recognition modules, wherein different recognition modules recognize different target objects.

5. The data processing method according to claim 3, characterized in that: The identifying the environment image based on the pre-trained model to determine the type of the target area and the attribute information of the target object in the environment image includes: Preprocess the environment image; The preprocessed environment image is passed into the pre-trained model, and the type of the environment image is determined based on the image classification module in the pre-trained model; After determining the type of the environmental image, the environmental image is transmitted to a corresponding recognition module, and attribute information of a target object in the environmental image is determined based on the recognition module; wherein the attribute information includes at least one of quantity information and status information of the target object.

6. The data processing method according to claim 1, characterized in that: The determining the operating conditions of the target area according to the attribute information of the target object and the resource usage information includes: Determine first information of a target area based on the attribute information of the target object; Determine second information of the target area based on the resource usage information of the target object; Determine an evaluation result of the target area based on the first information and the second information; Among them, the first information is used to reflect the change trend of the target object attribute information; the second information is used to reflect the change trend of the resource usage information.

7. The data processing method according to claim 5, characterized in that: The determining the first information of the target area based on the attribute information of the target object includes: Acquire first reference information of the target area; wherein the first reference information includes historical attribute information of the target object in the target area and / or attribute information of the target object in the same type area; First information of the target area is determined based on the first reference information and the attribute information.

8. The data processing method according to claim 5, characterized in that: The determining the second information of the target area based on the resource usage information of the target object includes: Acquire second reference information of the target area; wherein the second reference information includes historical resource information of the target area and / or resource information of areas of the same type; Second information of the target area is determined based on the second reference information and the resource usage.

9. A data processing device, characterized in that: The device comprises: A first acquisition module is used to acquire an environmental image of a target area; wherein the environmental image is determined based on photographic data that has passed authenticity verification; A recognition module, used to recognize the environmental image based on a pre-trained model, and determine the type of the target area and the attribute information of the target object in the environmental image; wherein the target objects of different types of target areas are different; A second acquisition module is used to acquire resource usage information of the target area; wherein the resource usage information includes at least one of water usage information and electricity usage information; and A determination module is used to determine the evaluation result of the target area according to the attribute information of the target object and the resource usage information; wherein the evaluation result is used to reflect the operating status of the target area.

10. An electronic device, comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

12. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

Images