WO2021027156A1 - 基于视频的农作物查勘方法、装置及计算机设备 - Google Patents

基于视频的农作物查勘方法、装置及计算机设备 Download PDF

Info

Publication number
WO2021027156A1
WO2021027156A1 PCT/CN2019/118245 CN2019118245W WO2021027156A1 WO 2021027156 A1 WO2021027156 A1 WO 2021027156A1 CN 2019118245 W CN2019118245 W CN 2019118245W WO 2021027156 A1 WO2021027156 A1 WO 2021027156A1
Authority
WO
WIPO (PCT)
Prior art keywords
assisting
video
requesting
picture
terminal
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2019/118245
Other languages
English (en)
French (fr)
Inventor
詹友能
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Ping An Technology Shenzhen Co Ltd
Original Assignee
Ping An Technology Shenzhen Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Ping An Technology Shenzhen Co Ltd filed Critical Ping An Technology Shenzhen Co Ltd
Publication of WO2021027156A1 publication Critical patent/WO2021027156A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/903—Querying
    • G06F16/90335—Query processing
    • G06F16/90344—Query processing by using string matching techniques
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/26—Speech to text systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
    • H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
    • Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture

Definitions

  • This application relates to the field of image recognition technology, and in particular to a video-based crop survey method, device and computer equipment.
  • Insurance survey refers to the comprehensive analysis of the insured target through scientific and systematic professional inspection, testing and survey methods, and then the scientific and systematic valuation of damages.
  • crops as the subject of insurance have been promoted to a certain extent. If the insured has suffered damage to the crops after insuring the crops, the surveyor of the insurance company is required to investigate the damage on the spot.
  • the surveyors have high requirements for professional knowledge in the agricultural field, which makes it impossible to accurately assess the risk situation by themselves.
  • consulting professionals are involved in the damage assessment, it is impossible to establish contact with professionals in real time, resulting in low processing efficiency.
  • the embodiments of this application provide a video-based crop survey method, device, computer equipment, and storage medium, which are designed to solve the problem that in the prior art, when surveying and determining the damage of the insured object of crops, the damage is generally determined manually due to lack of professional knowledge. The result of loss determination is inaccurate and the problem of inefficiency is handled.
  • an embodiment of the present application provides a video-based crop survey method, which includes:
  • the video information between the assisting end and the requesting end is obtained and saved; among them, the video information between the assisting end and the requesting end includes the corresponding information obtained by the assisting end Assisting end video data and requesting end video data correspondingly obtained by the requesting end; the requesting end video data includes crop video information and requester audio data; the assisting end video data includes assisting end audio data;
  • an embodiment of the present application provides a video-based crop survey device, which includes:
  • a location obtaining unit configured to obtain location information of the requesting end if a survey assistance request instruction issued by the requesting end is detected
  • the assisting terminal set acquiring unit is used to acquire the assisting terminal whose distance from the location information is within a preset distance threshold to form the assisting terminal set;
  • the video acquisition unit is used to obtain and save the video information between the assisting terminal and the requesting terminal if a successful video connection instruction is detected between the requesting terminal and the assisting terminal set; among them, the video information between the assisting terminal and the requesting terminal Including the assisting end video data correspondingly obtained by the assisting end and the requesting end video data correspondingly obtained by the requesting end; the requesting end video data includes crop video information and requester audio data; the assisting end video data includes the assisting end audio data;
  • An audio recognition unit configured to perform audio extraction on the video information to obtain an audio extraction result, and obtain the text of the audio extraction result through a voice recognition model to obtain the recognition result;
  • the result sending unit is configured to send the recognition result to the requesting end.
  • an embodiment of the present application provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and running on the processor, and the processor executes the computer
  • the program implements the video-based crop survey method described in the first aspect.
  • the embodiments of the present application also provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the above-mentioned The video-based crop survey method described in one aspect.
  • FIG. 1 is a schematic diagram of an application scenario of a video-based crop survey method provided by an embodiment of the application
  • FIG. 2 is a schematic flowchart of a video-based crop survey method provided by an embodiment of the application
  • FIG. 3 is a schematic diagram of another process of a video-based crop survey method provided by an embodiment of the application.
  • FIG. 4 is a schematic block diagram of a video-based crop survey device provided by an embodiment of the application.
  • FIG. 5 is another schematic block diagram of a video-based crop survey device provided by an embodiment of the application.
  • Fig. 6 is a schematic block diagram of a computer device provided by an embodiment of the application.
  • Figure 1 is a schematic diagram of an application scenario of a video-based crop surveying method provided by an embodiment of this application
  • Figure 2 is a schematic flowchart of a video-based crop surveying method provided by an embodiment of the application, which is video-based
  • the crop survey method of is applied to a server, and the method is executed by application software installed in the server.
  • the method includes steps S110 to S150.
  • Crop insurance is based on various artificially planted crops, including food crop insurance (mainly covering cereals, wheat, potato and legumes), cash crop insurance (mainly covering cotton, hemp, oil, sugar cane) Such as sugar, tobacco and medicinal crops), horticultural crop insurance (mainly covering fruits, vegetables and flowers and other crops).
  • Crop insurance can cover only one risk, or it can cover mixed liability and all risk liability. In the subject of insurance, it can be the insurance of the harvest value of crops (that is, 5-7 of the average annual harvest value of the past three years is used as the insured amount.
  • the insurance company will compensate the difference for the insufficient amount of insurance) It can also be crop production cost insurance (taking the production cost of planting crops as the subject of insurance, and the insurance company is responsible for compensating the actual cost loss of the crop after the disaster within the scope of the planting cost).
  • the following rules can be set:
  • the insurer is not responsible for compensation if the planted rice suffers from natural disasters within the scope of insurance liability, but the loss rate is below 30%.
  • the actual loss rate is between 30% (inclusive) and 70% to be paid proportionally, and 70% (including 70%) or more are paid in full.
  • the compensation shall be calculated according to the ratio of the insured area to the actual planting area.
  • the surveyor of the insurance company receives a claim from the insured, he needs to go to the scene to investigate the damage. If the investigator is unable to accurately assess the risk situation by himself during the on-site investigation, he can invite other salesmen in the company or experts outside the company to assist in the investigation process such as risk assessment and other investigation operations through video conference.
  • One is the requesting terminal which is an intelligent terminal used by the surveyor.
  • the surveyor arrives at the inspection site to survey the target crop, he can click the survey assistance virtual button on the requesting terminal to trigger the sending of a survey assistance request instruction to the server.
  • the server feedbacks the assisting terminal set to select one of the assisting terminals to establish a connection, online video can be carried out with the assisting terminal to assist in crop survey and damage assessment.
  • the second is the server, which is used to receive the survey assistance request instruction from the requesting end, feed back a set of eligible assistants according to the positioning information of the requesting end, and obtain and save the video information after the connection between the requesting end and the assisting end is established.
  • the saved video information can also be extracted from text information and sent to the requesting end after the recognition result is obtained, so that the requesting end can look back at the communication record after completing the contact with the assisting end.
  • the third is the assistance terminal, which is an intelligent terminal used by professionals in the field of crop claims.
  • the assisting terminal When the assisting terminal establishes a connection with the requesting terminal for online video, it can assist the surveyor to determine the damage on-site and improve the efficiency of viewing the damage.
  • the server detects the survey assistance request instruction sent by the requesting end, it obtains the location information of the requesting end (The positioning information is latitude and longitude information).
  • the server when the server receives the survey assistance request instruction and positioning information from the requesting end, in order to recommend other salespersons or experts in the library who have a better understanding of this area, it can obtain the distance from the location information A collection of assisting ends composed of assisting ends within a preset distance threshold (for example, setting the distance threshold to 30KM). Since the server can quickly query the set of assisting terminals that meet the conditions, the efficiency of obtaining the basic data required before the requesting terminal and the assisting terminal quickly establish a video connection is improved.
  • a preset distance threshold for example, setting the distance threshold to 30KM
  • step S120 the method further includes:
  • the target label is used to form the target label set
  • the target tag set whose similarity with the requested tag exceeds the similarity threshold among the tags corresponding to each assisting end in the assisting end set can be calculated, and the assisting end corresponding to the target tag set is used as the updated assisting end set.
  • acquiring the similarity between the tag corresponding to each assisting end of the set of assisting ends and the requested tag includes:
  • the string edit distance between the two tags can be calculated as the similarity between the two tags.
  • the string edit distance is the minimum number of times required to edit a single character (such as modification, insertion, deletion) when changing from one string to another. For example, to modify the string "kitten" to the string “sitting" only three single-character editing operations, such as sitten (k ⁇ s), sittin (e ⁇ i), sittin (_ ⁇ g), so "kitten The editing distance between "" and “sitting” is 3.
  • the video information between the assisting end and the requesting end is obtained and saved; wherein the video information between the assisting end and the requesting end includes the assisting end correspondence
  • the requesting end video data includes crop video information and requester audio data;
  • the assisting end video data includes assisting end audio data.
  • a successful video connection instruction of an assisting end in the requesting end and the assisting end set is detected, it means that the requesting end has established a video connection with the selected cooperative end.
  • the server is used to obtain the requesting end's video information and Video information on the assisting side.
  • the video information of the assistant terminal is saved for subsequent processing of the video information through the background of the server, such as voice recognition.
  • the server When the server saves the video information between the assisting end and the requesting end, it generates a serial number with the user ID corresponding to the requesting end (such as the surveyor’s job number), the user ID corresponding to the assisting end, and the current system time, and uses the serial number Create a new folder for the file name in the storage area of the server, and save the video information between the assisting end and the requesting end in the new folder.
  • a serial number with the user ID corresponding to the requesting end (such as the surveyor’s job number), the user ID corresponding to the assisting end, and the current system time
  • the user ID corresponding to the requesting end, the user ID corresponding to the assisting end and the current system time generation serial number can be saved in the corresponding folder to save the video data corresponding to the requesting end, and the assisting end The corresponding video data.
  • the server will view the corresponding feedback data according to the requesting terminal's data view request, thus realizing the effective preservation of historical data.
  • S140 Perform audio extraction on the video information to obtain an audio extraction result, and obtain a text of the audio extraction result through a voice recognition model to obtain a recognition result.
  • the audio extraction result can be obtained by removing the video channel information in the video information.
  • the audio extraction result is recognized by the voice recognition model to obtain the recognition result.
  • the recognition result is extracted to ensure that after the requesting end and the assisting end interrupt communication, in order to facilitate the requesting end to obtain the detailed text information of the previous video communication process to review the survey plan, at this time, the video information of the assisting end can be audio extracted through the server , Obtain the audio extraction result, obtain the text of the audio extraction result through the speech recognition model, and obtain the recognition result.
  • step S140 includes:
  • the audio extraction result is recognized through the N-gram model to obtain the recognition result.
  • the whole sentence is obtained by recognition, for example, "The loss rate of XX farm Y field is more than 30%".
  • the N-gram model can effectively recognize the speech to be recognized, and obtain the sentence with the largest recognition probability as the recognition result.
  • step S140 it also includes:
  • the training set corpus is received, and the training set corpus is input to the initial N-gram model for training to obtain an N-gram model; wherein, the N-gram model is an N-gram model.
  • the training set corpus is a general corpus, and the vocabulary in the general corpus is not biased towards a specific field, but vocabulary in each field is involved.
  • the N-gram model for speech recognition can be obtained by inputting the training set corpus to the initial N-gram model for training.
  • the method further includes:
  • the server after the server saves the video information, in addition to extracting the text of the audio extraction result in the video information, it can also perform image recognition on the video information to determine the crops in the video information. Category, using the crop category as the attribute tag of the recognition result.
  • the step of acquiring the crop category existing in the video information through image recognition specifically includes:
  • Pearson similarity calculation is performed on the picture feature vector corresponding to each picture in the picture set and the feature vector of each picture in the pre-built picture library to obtain the Pearson similarity of the picture feature vector corresponding to each picture in the picture set
  • the feature vector whose degree is greater than the preset similarity threshold is used as the feature vector of the retrieval result;
  • the retrieval result picture corresponding to the retrieval result feature vector in the picture library and the crop category label corresponding to the retrieval result picture are acquired, and the crop category label corresponding to the retrieval result picture is taken as the crop category existing in the video information.
  • the starting time point such as the 15th second
  • the acquisition duration such as 15 seconds
  • the target video segment is acquired from the video information according to the start time point and the acquisition duration. For example, at this time, a 15-second-long video is acquired from the 15th second from the video information corresponding to the requesting end as the target video segment.
  • multiple frames of pictures in the target video segment are obtained through video splitting to form a target picture set.
  • obtaining the picture feature vector of each target picture first obtain the pixel matrix corresponding to each target picture, and then divide each The pixel matrix corresponding to the target image is used as the input of the input layer in the convolutional neural network model to obtain multiple feature maps, and then the feature maps are input to the pooling layer to obtain the one-dimensional vector corresponding to the maximum value corresponding to each feature map, and finally The one-dimensional vector corresponding to the maximum value corresponding to each feature map is input to the fully connected layer to obtain a picture feature vector corresponding to each target picture.
  • the feature templates stored in the image library store the feature vectors of a large number of people pictures that have been collected, with these mass feature templates as a data basis, they can be used to determine the crop category corresponding to the target image, thereby achieving image recognition .
  • the server when the server has completed the text extraction of the audio extraction result, it sends the recognition result to the requesting end, and the salesperson corresponding to the requesting end can obtain the requirements for claim settlement according to the survey plan in the recognition result. Obtain important parameters to realize on-site survey.
  • This method realizes the inviting professional online video to assist in the damage assessment in the process of damage assessment, and improves the accuracy and efficiency of the damage assessment result.
  • the embodiments of the present application also provide a video-based crop surveying device, which is used to execute any embodiment of the aforementioned video-based crop surveying method.
  • FIG. 4 is a schematic block diagram of a video-based crop survey device provided by an embodiment of the present application.
  • the video-based crop survey device 100 can be configured in a server.
  • the video-based crop surveying device 100 includes a positioning acquisition unit 110, an assistance terminal collection acquisition unit 120, a video acquisition unit 130, an audio recognition unit 140, and a result sending unit 150.
  • the location obtaining unit 110 is configured to obtain location information of the requesting end if a survey assistance request instruction issued by the requesting end is detected.
  • the insurer is not responsible for compensation if the planted rice suffers from natural disasters within the scope of insurance liability, but the loss rate is below 30%.
  • the actual loss rate is between 30% (inclusive) and 70% to be paid proportionally, and 70% (including 70%) or more are paid in full.
  • the compensation shall be calculated according to the ratio of the insured area to the actual planting area.
  • the surveyor of the insurance company receives a claim from the insured, he needs to go to the scene to investigate the damage. If the investigator is unable to accurately assess the risk situation by himself during the on-site investigation, he can invite other salesmen in the company or experts outside the company to assist in the investigation process such as risk assessment and other investigation operations through video conference.
  • the server detects the survey assistance request instruction sent by the requesting end, it obtains the location information of the requesting end (The positioning information is latitude and longitude information).
  • the assisting terminal set acquiring unit 120 is configured to acquire the assisting terminals whose distance from the located information is within a preset distance threshold to form the assisting terminal set.
  • the server when the server receives the survey assistance request instruction and positioning information from the requesting end, in order to recommend other salespersons or experts in the library who have a better understanding of this area, it can obtain the distance from the location information A collection of assisting ends composed of assisting ends within a preset distance threshold (for example, setting the distance threshold to 30KM). Since the server can quickly query the set of assisting terminals that meet the conditions, the efficiency of obtaining the basic data required before the requesting terminal and the assisting terminal can quickly establish a video connection is improved.
  • a preset distance threshold for example, setting the distance threshold to 30KM
  • the video-based crop survey device 100 further includes:
  • a similarity obtaining unit configured to obtain a request label corresponding to the survey assistance request, and obtain the similarity between the label corresponding to each assisting end of the set of assisting ends and the request label;
  • a target tag set obtaining unit configured to form a target tag set with the target tags if there are target tags whose similarity with the requested tag exceeds a preset similarity threshold among the tags corresponding to each assisting end of the assisting end set;
  • the set update unit is used to obtain the assisting terminal corresponding to the target tag set to obtain the updated assisting terminal set.
  • the target tag set whose similarity with the requested tag exceeds the similarity threshold among the tags corresponding to each assisting end in the assisting end set can be calculated, and the assisting end corresponding to the target tag set is used as the updated assisting end set.
  • the similarity acquisition unit is further configured to:
  • the string edit distance between the two tags can be calculated as the similarity between the two tags.
  • the string edit distance is the minimum number of times required to edit a single character (such as modification, insertion, deletion) when changing from one string to another. For example, to modify the string "kitten" to the string “sitting" only three single-character editing operations, such as sitten (k ⁇ s), sittin (e ⁇ i), sittin (_ ⁇ g), so "kitten The editing distance between "" and “sitting” is 3.
  • the video acquisition unit 130 is configured to obtain and save the video information between the assisting terminal and the requesting terminal if a successful video connection instruction is detected between the requesting terminal and the assisting terminal set; among them, the video between the assisting terminal and the requesting terminal
  • the information includes the assisting end video data corresponding to the assisting end and the requesting end video data corresponding to the requesting end; the requesting end video data includes crop video information and requester audio data; the assisting end video data includes the assisting end audio data .
  • a successful video connection instruction of an assisting end in the requesting end and the assisting end set is detected, it means that the requesting end has established a video connection with the selected cooperative end.
  • the server is used to obtain the requesting end's video information and Video information on the assisting side.
  • the video information of the assistant terminal is saved for subsequent processing of the video information through the background of the server, such as voice recognition.
  • the server When the server saves the video information between the assisting end and the requesting end, it generates a serial number with the user ID corresponding to the requesting end (such as the surveyor’s job number), the user ID corresponding to the assisting end, and the current system time, and uses the serial number Create a new folder for the file name in the storage area of the server, and save the video information between the assisting end and the requesting end in the new folder.
  • a serial number with the user ID corresponding to the requesting end (such as the surveyor’s job number), the user ID corresponding to the assisting end, and the current system time
  • the user ID corresponding to the requesting end, the user ID corresponding to the assisting end and the current system time generation serial number can be saved in the corresponding folder to save the video data corresponding to the requesting end, and the assisting end The corresponding video data.
  • the server will view the corresponding feedback data according to the requesting terminal's data view request, thus realizing the effective storage of historical data.
  • the audio recognition unit 140 is configured to perform audio extraction on the video information to obtain an audio extraction result, and obtain the text of the audio extraction result through a voice recognition model to obtain the recognition result.
  • the audio extraction result can be obtained by removing the video channel information in the video information.
  • the audio extraction result is recognized by the voice recognition model to obtain the recognition result.
  • the recognition result is extracted to ensure that after the requesting end and the assisting end interrupt communication, in order to facilitate the requesting end to obtain the detailed text information of the previous video communication process to review the survey plan, at this time, the video information of the assisting end can be audio extracted through the server , Obtain the audio extraction result, obtain the text of the audio extraction result through the speech recognition model, and obtain the recognition result.
  • the audio recognition unit 140 is further used to:
  • the audio extraction result is recognized through the N-gram model to obtain the recognition result.
  • the whole sentence is obtained by recognition, for example, "The loss rate of XX farm Y field is more than 30%".
  • the N-gram model can effectively recognize the speech to be recognized, and obtain the sentence with the largest recognition probability as the recognition result.
  • video-based crop survey device 100 further includes:
  • the model training unit is configured to receive a training set corpus, and input the training set corpus to the initial N-gram model for training to obtain an N-gram model; wherein the N-gram model is an N-gram model.
  • the training set corpus is a general corpus, and the vocabulary in the general corpus is not biased towards a specific field, but vocabulary in each field is involved.
  • the N-gram model for speech recognition can be obtained by inputting the training set corpus to the initial N-gram model for training.
  • the video-based crop survey device 100 further includes:
  • the crop category identifying unit 141 is configured to obtain the crop category existing in the video information through image recognition, and use the crop category as the attribute tag of the recognition result.
  • the server after the server saves the video information, in addition to extracting the text of the audio extraction result in the video information, it can also perform image recognition on the video information to determine the crops in the video information. Category, using the crop category as the attribute tag of the recognition result.
  • the crop category identification unit 141 includes:
  • a target video segment acquiring unit configured to acquire a target video segment in the video information according to a preset starting time point and acquisition duration
  • a video splitting unit configured to obtain multiple frames of pictures in the target video segment through video splitting to form a target picture set
  • the feature vector obtaining unit is configured to perform feature extraction on each picture in the target picture set through a convolutional neural network model to obtain a picture feature vector corresponding to each picture in the target picture set;
  • the retrieval result feature vector acquiring unit is used to calculate the Pearson similarity between the feature vector of each picture corresponding to each picture in the picture set and the feature vector of each picture in the pre-built picture library, and obtain each picture in the picture set.
  • the feature vector whose Pearson similarity of the corresponding image feature vector is greater than the preset similarity threshold is used as the feature vector of the retrieval result;
  • the crop category label obtaining unit is configured to obtain the retrieval result picture corresponding to the retrieval result feature vector in the picture library and the crop category label corresponding to the retrieval result picture, and use the crop category label corresponding to the retrieval result picture as the video information
  • the starting time point such as the 15th second
  • the acquisition duration such as 15 seconds
  • the target video segment is acquired from the video information according to the start time point and the acquisition duration. For example, at this time, a 15-second-long video is acquired from the 15th second from the video information corresponding to the requesting end as the target video segment.
  • multiple frames of pictures in the target video segment are obtained through video splitting to form a target picture set.
  • obtaining the picture feature vector of each target picture first obtain the pixel matrix corresponding to each target picture, and then divide each The pixel matrix corresponding to the target image is used as the input of the input layer in the convolutional neural network model to obtain multiple feature maps, and then the feature maps are input to the pooling layer to obtain the one-dimensional vector corresponding to the maximum value corresponding to each feature map, and finally The one-dimensional vector corresponding to the maximum value corresponding to each feature map is input to the fully connected layer to obtain a picture feature vector corresponding to each target picture.
  • the feature templates stored in the image library store the feature vectors of a large number of people pictures that have been collected, with these mass feature templates as a data basis, they can be used to determine the crop category corresponding to the target image, thereby achieving image recognition .
  • the result sending unit 150 is configured to send the recognition result to the requesting end.
  • the server when the server has completed the text extraction of the audio extraction result, it sends the recognition result to the requesting end, and the salesperson corresponding to the requesting end can obtain the requirements for claim settlement according to the survey plan in the recognition result. Obtain important parameters to realize on-site survey.
  • the device realizes the inviting professional online video to assist in the damage assessment during the damage investigation and assessment process, and improves the accuracy of the investigation and assessment results and the efficiency of the investigation and assessment.
  • the above-mentioned video-based crop surveying device can be implemented in the form of a computer program, and the computer program can be run on a computer device as shown in FIG. 6.
  • FIG. 6 is a schematic block diagram of a computer device according to an embodiment of the present application.
  • the computer device 500 is a server, and the server may be an independent server or a server cluster composed of multiple servers.
  • the computer device 500 includes a processor 502, a memory, and a network interface 505 connected through a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.
  • the non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032.
  • the processor 502 can execute a video-based crop survey method.
  • the processor 502 is used to provide calculation and control capabilities, and support the operation of the entire computer device 500.
  • the internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503.
  • the processor 502 can execute the video-based crop survey method.
  • the network interface 505 is used for network communication, such as providing data information transmission.
  • the structure shown in FIG. 6 is only a block diagram of part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied.
  • the specific computer device 500 may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.
  • the processor 502 is configured to run a computer program 5032 stored in a memory to implement the video-based crop survey method in the embodiment of the present application.
  • the embodiment of the computer device shown in FIG. 6 does not constitute a limitation on the specific configuration of the computer device.
  • the computer device may include more or less components than those shown in the figure. Or combine certain components, or different component arrangements.
  • the computer device may only include a memory and a processor. In such embodiments, the structures and functions of the memory and the processor are the same as those of the embodiment shown in FIG. 6, which will not be repeated here.
  • the processor 502 may be a central processing unit (Central Processing Unit, CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (Digital Signal Processors, DSP), Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • the general-purpose processor may be a microprocessor or the processor may also be any conventional processor.
  • a computer-readable storage medium may be a non-volatile computer-readable storage medium.
  • the computer-readable storage medium stores a computer program, where the computer program is executed by a processor to implement the video-based crop survey method in the embodiments of the present application.
  • the storage medium is a physical, non-transitory storage medium, such as a U disk, a mobile hard disk, a read-only memory (Read-Only Memory, ROM), a magnetic disk, or an optical disk that can store program codes. medium.
  • a physical, non-transitory storage medium such as a U disk, a mobile hard disk, a read-only memory (Read-Only Memory, ROM), a magnetic disk, or an optical disk that can store program codes. medium.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Business, Economics & Management (AREA)
  • Databases & Information Systems (AREA)
  • Multimedia (AREA)
  • General Business, Economics & Management (AREA)
  • Computational Linguistics (AREA)
  • Library & Information Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Marketing (AREA)
  • Data Mining & Analysis (AREA)
  • Strategic Management (AREA)
  • Finance (AREA)
  • Health & Medical Sciences (AREA)
  • Accounting & Taxation (AREA)
  • Economics (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Technology Law (AREA)
  • Mining & Mineral Resources (AREA)
  • Human Computer Interaction (AREA)
  • Acoustics & Sound (AREA)
  • Animal Husbandry (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Tourism & Hospitality (AREA)
  • Development Economics (AREA)
  • Marine Sciences & Fisheries (AREA)
  • Signal Processing (AREA)
  • Agronomy & Crop Science (AREA)
  • Primary Health Care (AREA)
  • Human Resources & Organizations (AREA)
  • General Health & Medical Sciences (AREA)
  • Image Analysis (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

本申请公开了基于视频的农作物查勘方法、装置、计算机设备及存储介质。该方法包括：若检测到请求端发出的查勘协助请求指令，获取所述请求端的定位信息；获取与所定位信息之间的间距在预设的距离阈值之内的协助端，以组成协助端集合；若检测到请求端与协助端集合中一个协助端的视频连接成功指令，获取协助端与请求端之间的视频信息并保存；对所述视频信息进行音频提取，得到音频提取结果，通过语音识别模型获取所述音频提取结果的文本以得到识别结果；以及将所述识别结果发送至所述请求端。

Description

基于视频的农作物查勘方法、装置及计算机设备
本申请要求于2019年8月15日提交中国专利局、申请号为201910752906.1、申请名称为“基于视频的农作物查勘方法、装置及计算机设备”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及图像识别技术领域，尤其涉及一种基于视频的农作物查勘方法、装置及计算机设备。
背景技术
保险查勘是指通过科学、系统的专业化检查、测试与勘测手段，对投保标的进行综合分析后进行科学系统的估损定价。目前在农业领域中，农作物作为标的的保险得到了一定推广，若投保人对农作物投保后有受灾损失发生时，需要保险公司的查勘员去现场查勘定损。查勘员在查勘过程中，由于对农业领域专业性知识要求较高，导致无法独自对出险状况进行准确评估。而且查勘定损中若涉及到咨询专业人员，无法及时与专业人员实时建立联系，导致处理效率低下。
发明内容
本申请实施例提供了一种基于视频的农作物查勘方法、装置、计算机设备及存储介质，旨在解决现有技术中对农作物投保标的物查勘定损时一般是人工定损，因专业知识欠缺导致定损结果不准确，而且处理效率低下的问题。
第一方面，本申请实施例提供了一种基于视频的农作物查勘方法，其包括：
若检测到请求端发出的查勘协助请求指令，获取所述请求端的定位信息；
获取与所定位信息之间的间距在预设的距离阈值之内的协助端，以组成协助端集合；
若检测到请求端与协助端集合中一个协助端的视频连接成功指令，获取协助端与请求端之间的视频信息并保存；其中，协助端与请求端之间的视频信息包括协助端对应获取的协助端视频数据、以及请求端对应获取的请求端视频数 据；所述请求端视频数据包括农作物视频信息及请求人音频数据；所述协助端视频数据包括协助端音频数据；
对所述视频信息进行音频提取，得到音频提取结果，通过语音识别模型获取所述音频提取结果的文本以得到识别结果；以及
将所述识别结果发送至所述请求端。
第二方面，本申请实施例提供了一种基于视频的农作物查勘装置，其包括：
定位获取单元，用于若检测到请求端发出的查勘协助请求指令，获取所述请求端的定位信息；
协助端集合获取单元，用于获取与所定位信息之间的间距在预设的距离阈值之内的协助端，以组成协助端集合；
视频获取单元，用于若检测到请求端与协助端集合中一个协助端的视频连接成功指令，获取协助端与请求端之间的视频信息并保存；其中，协助端与请求端之间的视频信息包括协助端对应获取的协助端视频数据、以及请求端对应获取的请求端视频数据；所述请求端视频数据包括农作物视频信息及请求人音频数据；所述协助端视频数据包括协助端音频数据；
音频识别单元，用于对所述视频信息进行音频提取，得到音频提取结果，通过语音识别模型获取所述音频提取结果的文本以得到识别结果；以及
结果发送单元，用于将所述识别结果发送至所述请求端。
第三方面，本申请实施例又提供了一种计算机设备，其包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现上述第一方面所述的基于视频的农作物查勘方法。
第四方面，本申请实施例还提供了一种计算机可读存储介质，其中所述计算机可读存储介质存储有计算机程序，所述计算机程序当被处理器执行时使所述处理器执行上述第一方面所述的基于视频的农作物查勘方法。
附图说明
为了更清楚地说明本申请实施例技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本申请实施例提供的基于视频的农作物查勘方法的应用场景示意图；
图2为本申请实施例提供的基于视频的农作物查勘方法的流程示意图；
图3为本申请实施例提供的基于视频的农作物查勘方法的另一流程示意图；
图4为本申请实施例提供的基于视频的农作物查勘装置的示意性框图；
图5为本申请实施例提供的基于视频的农作物查勘装置的另一示意性框图；
图6为本申请实施例提供的计算机设备的示意性框图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
应当理解，当在本说明书和所附权利要求书中使用时，术语“包括”和“包含”指示所描述特征、整体、步骤、操作、元素和/或组件的存在，但并不排除一个或多个其它特征、整体、步骤、操作、元素、组件和/或其集合的存在或添加。
还应当理解，在此本申请说明书中所使用的术语仅仅是出于描述特定实施例的目的而并不意在限制本申请。如在本申请说明书和所附权利要求书中所使用的那样，除非上下文清楚地指明其它情况，否则单数形式的“一”、“一个”及“该”意在包括复数形式。
还应当进一步理解，在本申请说明书和所附权利要求书中使用的术语“和/或”是指相关联列出的项中的一个或多个的任何组合以及所有可能组合，并且包括这些组合。
请参阅图1和图2，图1为本申请实施例提供的基于视频的农作物查勘方法的应用场景示意图；图2为本申请实施例提供的基于视频的农作物查勘方法的流程示意图，该基于视频的农作物查勘方法应用于服务器中，该方法通过安装于服务器中的应用软件进行执行。
如图2所示，该方法包括步骤S110～S150。
S110、若检测到请求端发出的查勘协助请求指令，获取所述请求端的定位信息。
在本实施例中，用户可以向保险公司对农作物投保。农作物保险是以人工 种植的各种农作物为标的的保险，包括粮食作物保险(主要承保谷类、麦类、薯类和豆类作物)，经济作物保险(主要承保棉花、麻类、油类、甘蔗等糖类、烟和药用类作物)，园艺作物保险(主要承保瓜果、蔬菜和花卉等作物)。农作物保险，可只承保一项风险，也可承保混合责任和一切风险责任。在保险标的上，可以是农作物收获量产值的保险(即以近三年平均常年产量的收获价值的5～7成为保险金额，由于作物受灾损失，其不足保额部分，由保险公司赔偿其差额)，也可以是农作物生产成本保险(以种植作物的生产成本为保险标的，保险公司负责在种植成本费范围内对作物遭灾后的实际成本损失给予赔偿)。
当用户对指定位置(如XX农场Y号田)的农作物(如水稻)投保，例如可以设置以下规则：
a)种植的水稻因遭受保险责任范围内的自然灾害事故，但损失率在30％以下，保险人不负责赔偿。
b)实际损失率在30％(含)-70％按比例赔付，70％(含70％)以上全额赔偿。每位被保险人保险水稻地块面积小于实际种植面积时，按承保面积占实际种植面积的比例计算赔偿。
若保险公司的查勘员在接收到了被保险人的理赔请求时，需要去现场查勘定损。若该查勘员在现场查勘时无法独自对出险状况进行准确评估，可以通过视频会议的方式，邀请司内其他业务员或者司外专家来协助进行出险评估等查勘作业流程。
为了更清楚的理解技术方案，将所涉及到的终端进行详细介绍。本申请中，是站在服务器的角度来描述技术方案。
一是请求端，其为查勘员所使用的智能终端。当查勘员到达查看现场对农作物标的进行查勘时，可以点击请求端上的查勘协助虚拟按键，以触发向服务器发送查勘协助请求指令。而且在服务器反馈了协助端集合选择其中一个协助端建立连接后，即可与协助端进行在线视频以辅助农作物查勘定损。
二是服务器，用于接收请求端的查勘协助请求指令，根据请求端的定位信息反馈符合条件的协助端集合，而且在请求端与协助端建立连接后获取视频信息并保存。所保存的视频信息还可提取文本信息得到识别结果后发送至请求端，以供请求端在于协助端联系完成后回看交流记录。
三是协助端，其为农作物标的理赔领域专业人员所使用的智能终端。当协 助端与请求端建立连接进行在线视频时，可以辅助查勘员现场定损，提高查看定损效率。
此时当在查勘现场的业务员手持的请求端(如智能手机，平板电脑)向服务器发出查勘协助请求指令，若服务器器检测到请求端发出的查勘协助请求指令，获取所述请求端的定位信息(定位信息为经纬度信息)。
S120、获取与所定位信息之间的间距在预设的距离阈值之内的协助端，以组成协助端集合。
在本实施例中，当服务器接收了请求端的所述查勘协助请求指令及定位信息时，为了推荐更了解此块区域的其他业务员或是在库专家，可以获取与所定位信息之间的间距在预设的距离阈值(如将距离阈值设置为30KM)之内的协助端组成的协助端集合。由于服务器能快速查询满足条件的协助端集合，提高了请求端与协助端快速建立视频连接之前所需基础数据的获取效率。
在一实施例中，步骤S120之后还包括：
获取与所述查勘协助请求对应的请求标签，并获取所述协助端集合各协助端对应的标签与所述请求标签之间的相似度；
若所述协助端集合各协助端对应的标签中存在与所述请求标签的相似度超出预设的相似度阈值的目标标签，以目标标签组成目标标签集合；
获取与所述目标标签集合对应的协助端，以得到更新后的协助端集合。
在本实施例中，由于对每一协助端设置了标签(该标签用于表示该协助端所擅长的理赔领域)，当获知了请求标签后，为了更精准的对请求端推荐协助端，此时可以计算协助端集合各协助端对应的标签中与请求标签的相似度超出相似度阈值的目标标签集合，以目标标签集合对应的协助端作为更新后的协助端集合。
在一实施例中，获取所述协助端集合各协助端对应的标签与所述请求标签之间的相似度时，包括：
获取所述协助端集合各协助端对应的标签与所述请求标签之间的字符串编辑距离，以作为所述协助端集合各协助端对应的标签与所述请求标签之间的相似度。
具体的，在计算算协助端集合各协助端对应的标签中与请求标签的相似度时，可以计算两个标签之间的字符串编辑距离以作为两个标签之间的相似度。 字符串编辑距离就是从一个字符串修改到另一个字符串时，其中编辑单个字符(比如修改、插入、删除)所需要的最少次数。例如，从字符串“kitten”修改为字符串“sitting”只需3次单字符编辑操作，具体如sitten(k→s)、sittin(e→i)、sittin(_→g)，因此“kitten”和“sitting”的字符串编辑距离为3。
S130、若检测到请求端与协助端集合中一个协助端的视频连接成功指令，获取协助端与请求端之间的视频信息并保存；其中，协助端与请求端之间的视频信息包括协助端对应获取的协助端视频数据、以及请求端对应获取的请求端视频数据；所述请求端视频数据包括农作物视频信息及请求人音频数据；所述协助端视频数据包括协助端音频数据。
在本实施例中，若检测到请求端与协助端集合中一个协助端的视频连接成功指令时，表示请求端与选定的协作端建立了视频连接，此时服务器用于获取请求端的视频信息和协助端的视频信息。保存协助端的视频信息，是为了通过服务器这一后台对视频信息进行后续处理，例如语音识别等。
服务器在保存协助端与请求端之间的视频信息时，以请求端对应的用户ID(如查勘员的工号)、协助端对应的用户ID及当前系统时间生成一个流水号，并以该流水号为文件名称在服务器的存储区域新建文件夹，将协助端与请求端之间的视频信息保存在该新建文件夹。
为了更完整的记录请求端与协助端的视频沟通过程，可以请求端对应的用户ID、协助端对应的用户ID与当前系统时间生成流水号对应文件夹中保存请求端对应的视频数据、以及协助端对应的视频数据。当请求端后期需查看与协助端之间的视频数据或视频数据对应的音频数据时，服务器根据请求端的数据查看请求对应反馈数据，实现了对历史数据的有效保存。
S140、对所述视频信息进行音频提取，得到音频提取结果，通过语音识别模型获取所述音频提取结果的文本以得到识别结果。
在本实施例中，去除视频信息中的视频通道信息即可得到音频提取结果，此时通过语音识别模型对所述音频提取结果进行识别，得到识别结果。提取识别结果是为了确保请求端与协助端中断沟通之后，为了便于请求端得到之前视频交流过程中的详细文字信息以重温查勘方案，此时可以通过服务器对所述协助端的视频信息进行音频提取，得到音频提取结果，通过语音识别模型获取所述音频提取结果的文本，得到识别结果。
在一实施例中，步骤S140包括：
通过N-gram模型对所述音频提取结果进行识别，以得到识别结果。
在本实施例中，当通过所述N-gram模型对所述待识别语音进行进行识别，识别得到的是一整句话，例如“XX农场Y号田的损失率在30％以上”，通过N-gram模型能对所述待识别语音进行进行有效识别，得到识别概率最大的语句作为识别结果。
且在步骤S140之前还包括：
接收训练集语料库，将所述训练集语料库输入至初始N-gram模型进行训练，得到N-gram模型；其中，所述N-gram模型为N元模型。
在本实施例中，训练集语料库是通用语料库，通用语料中的词汇并未偏向于某一具体领域，而是每一领域的词汇都有涉及。通过所述训练集语料库输入至初始N-gram模型进行训练，即可得到用于语音识别的N-gram模型。
在一实施例中，如图3所示，步骤S140之后还包括：
S141、通过图像识别获取所述视频信息中存在的农作物类别，将所述农作物类别作为所述识别结果的属性标签。
在本实施例中，当服务器保存了所述视频信息后，除了可以提取视频信息中的音频提取结果的文本，还可以对所述视频信息进行图像识别，以判断所述视频信息中存在的农作物类别，将所述农作物类别作为所述识别结果的属性标签。
在一实施例中，通过图像识别获取所述视频信息中存在的农作物类别的步骤具体包括：
根据预设的起始时间点和获取时长在所述视频信息中获取目标视频段；
通过视频拆分获取所述目标视频段中的多帧图片，以组成目标图片集；
通过卷积神经网络模型对所述目标图片集中各图片进行特征提取，得到与所述目标图片集中各图片对应的图片特征向量；
将与所述图片集中各图片对应的图片特征向量均与预先构建的图片库中各图片的特征向量进行皮尔逊相似度计算，获取与所述图片集中各图片对应的图片特征向量的皮尔逊相似度大于预设相似度阈值的特征向量以作为检索结果特征向量；
获取所述检索结果特征向量在所述图片库中对应的检索结果图片及检索结 果图片对应的农作物类别标签，将检索结果图片对应的农作物类别标签作为所述视频信息中存在的农作物类别。
在本实施例中，为了对视频信息进行图像识别且为了减少数据处理量，可以预先设置起始时间点(如第15秒)和获取时长(如15秒)，之后在所述请求端对应的视频信息中根据起始时间点和获取时长在所述视频信息中获取目标视频段。例如，此时从所述请求端对应的视频信息中由第15秒开始获取了15秒时长的视频作为目标视频段。
之后通过视频拆分获取所述目标视频段中的多帧图片，以组成目标图片集，在获取个目标图片的图片特征向量时，先获取与每一目标图片对应的像素矩阵，然后将每一目标图片对应的像素矩阵作为卷积神经网络模型中输入层的输入，得到多个特征图，之后将特征图输入池化层，得到每一特征图对应的最大值所对应一维向量，最后将每一特征图对应的最大值所对应一维向量输入至全连接层，得到与每一目标图片对应的图片特征向量。
由于图片库中已存储的特征模板中存储了已采集的海量的人图片的特征向量，有了这些海量的特征模板为数据基础后，可以用来确定目标图片对应的农作物类别，从而实现图像识别。
S150、将所述识别结果发送至所述请求端。
在本实施例中，当服务器完成了对所述音频提取结果的文本提取，将所述识别结果发送至所述请求端，请求端对应的业务员可以根据识别结果中的查勘方案获取理赔中需要得到的重要参数，从而实现现场查勘。
该方法实现了查勘定损过程中邀请专业人员在线视频以协助定损，提高了查勘定损结果的准确性以及查勘定损效率。
本申请实施例还提供一种基于视频的农作物查勘装置，该基于视频的农作物查勘装置用于执行前述基于视频的农作物查勘方法的任一实施例。具体地，请参阅图4，图4是本申请实施例提供的基于视频的农作物查勘装置的示意性框图。该基于视频的农作物查勘装置100可以配置于服务器中。
如图4所示，基于视频的农作物查勘装置100包括定位获取单元110、协助端集合获取单元120、视频获取单元130、音频识别单元140、结果发送单元150。
定位获取单元110，用于若检测到请求端发出的查勘协助请求指令，获取所述请求端的定位信息。
在本实施例中，当用户对指定位置(如XX农场Y号田)的农作物(如水稻)投保，例如可以设置以下规则：
a)种植的水稻因遭受保险责任范围内的自然灾害事故，但损失率在30％以下，保险人不负责赔偿。
b)实际损失率在30％(含)-70％按比例赔付，70％(含70％)以上全额赔偿。每位被保险人保险水稻地块面积小于实际种植面积时，按承保面积占实际种植面积的比例计算赔偿。
若保险公司的查勘员在接收到了被保险人的理赔请求时，需要去现场查勘定损。若该查勘员在现场查勘时无法独自对出险状况进行准确评估，可以通过视频会议的方式，邀请司内其他业务员或者司外专家来协助进行出险评估等查勘作业流程。
此时当在查勘现场的业务员手持的请求端(如智能手机，平板电脑)向服务器发出查勘协助请求指令，若服务器器检测到请求端发出的查勘协助请求指令，获取所述请求端的定位信息(定位信息为经纬度信息)。
协助端集合获取单元120，用于获取与所定位信息之间的间距在预设的距离阈值之内的协助端，以组成协助端集合。
在本实施例中，当服务器接收了请求端的所述查勘协助请求指令及定位信息时，为了推荐更了解此块区域的其他业务员或是在库专家，可以获取与所定位信息之间的间距在预设的距离阈值(如将距离阈值设置为30KM)之内的协助端组成的协助端集合。由于服务器能快速查询满足条件的协助端集合，提高了请求端与协助端快速建立视频连接之前所需基础数据的获取效率。
在一实施例中，所述基于视频的农作物查勘装置100还包括：
相似度获取单元，用于获取与所述查勘协助请求对应的请求标签，并获取所述协助端集合各协助端对应的标签与所述请求标签之间的相似度；
目标标签集合获取单元，用于若所述协助端集合各协助端对应的标签中存在与所述请求标签的相似度超出预设的相似度阈值的目标标签，以目标标签组成目标标签集合；
集合更新单元，用于获取与所述目标标签集合对应的协助端，以得到更新后的协助端集合。
在本实施例中，由于对每一协助端设置了标签(该标签用于表示该协助端 所擅长的理赔领域)，当获知了请求标签后，为了更精准的对请求端推荐协助端，此时可以计算协助端集合各协助端对应的标签中与请求标签的相似度超出相似度阈值的目标标签集合，以目标标签集合对应的协助端作为更新后的协助端集合。
在一实施例中，所述相似度获取单元还用于：
获取所述协助端集合各协助端对应的标签与所述请求标签之间的字符串编辑距离，以作为所述协助端集合各协助端对应的标签与所述请求标签之间的相似度。
具体的，在计算算协助端集合各协助端对应的标签中与请求标签的相似度时，可以计算两个标签之间的字符串编辑距离以作为两个标签之间的相似度。字符串编辑距离就是从一个字符串修改到另一个字符串时，其中编辑单个字符(比如修改、插入、删除)所需要的最少次数。例如，从字符串“kitten”修改为字符串“sitting”只需3次单字符编辑操作，具体如sitten(k→s)、sittin(e→i)、sittin(_→g)，因此“kitten”和“sitting”的字符串编辑距离为3。
视频获取单元130，用于若检测到请求端与协助端集合中一个协助端的视频连接成功指令，获取协助端与请求端之间的视频信息并保存；其中，协助端与请求端之间的视频信息包括协助端对应获取的协助端视频数据、以及请求端对应获取的请求端视频数据；所述请求端视频数据包括农作物视频信息及请求人音频数据；所述协助端视频数据包括协助端音频数据。
在本实施例中，若检测到请求端与协助端集合中一个协助端的视频连接成功指令时，表示请求端与选定的协作端建立了视频连接，此时服务器用于获取请求端的视频信息和协助端的视频信息。保存协助端的视频信息，是为了通过服务器这一后台对视频信息进行后续处理，例如语音识别等。
服务器在保存协助端与请求端之间的视频信息时，以请求端对应的用户ID(如查勘员的工号)、协助端对应的用户ID及当前系统时间生成一个流水号，并以该流水号为文件名称在服务器的存储区域新建文件夹，将协助端与请求端之间的视频信息保存在该新建文件夹。
为了更完整的记录请求端与协助端的视频沟通过程，可以请求端对应的用户ID、协助端对应的用户ID与当前系统时间生成流水号对应文件夹中保存请求端对应的视频数据、以及协助端对应的视频数据。当请求端后期需查看与协助 端之间的视频数据或视频数据对应的音频数据时，服务器根据请求端的数据查看请求对应反馈数据，实现了对历史数据的有效保存。
音频识别单元140，用于对所述视频信息进行音频提取，得到音频提取结果，通过语音识别模型获取所述音频提取结果的文本以得到识别结果。
在本实施例中，去除视频信息中的视频通道信息即可得到音频提取结果，此时通过语音识别模型对所述音频提取结果进行识别，得到识别结果。提取识别结果是为了确保请求端与协助端中断沟通之后，为了便于请求端得到之前视频交流过程中的详细文字信息以重温查勘方案，此时可以通过服务器对所述协助端的视频信息进行音频提取，得到音频提取结果，通过语音识别模型获取所述音频提取结果的文本，得到识别结果。
在一实施例中，音频识别单元140还用于：
通过N-gram模型对所述音频提取结果进行识别，以得到识别结果。
在本实施例中，当通过所述N-gram模型对所述待识别语音进行进行识别，识别得到的是一整句话，例如“XX农场Y号田的损失率在30％以上”，通过N-gram模型能对所述待识别语音进行进行有效识别，得到识别概率最大的语句作为识别结果。
且所述基于视频的农作物查勘装置100还包括：
模型训练单元，用于接收训练集语料库，将所述训练集语料库输入至初始N-gram模型进行训练，得到N-gram模型；其中，所述N-gram模型为N元模型。
在本实施例中，训练集语料库是通用语料库，通用语料中的词汇并未偏向于某一具体领域，而是每一领域的词汇都有涉及。通过所述训练集语料库输入至初始N-gram模型进行训练，即可得到用于语音识别的N-gram模型。
在一实施例中，如图5所示，所述基于视频的农作物查勘装置100还包括：
农作物类别识别单元141，用于通过图像识别获取所述视频信息中存在的农作物类别，将所述农作物类别作为所述识别结果的属性标签。
在本实施例中，当服务器保存了所述视频信息后，除了可以提取视频信息中的音频提取结果的文本，还可以对所述视频信息进行图像识别，以判断所述视频信息中存在的农作物类别，将所述农作物类别作为所述识别结果的属性标签。
在一实施例中，农作物类别识别单元141，包括：
目标视频段获取单元，用于根据预设的起始时间点和获取时长在所述视频信息中获取目标视频段；
视频拆分单元，用于通过视频拆分获取所述目标视频段中的多帧图片，以组成目标图片集；
特征向量获取单元，用于通过卷积神经网络模型对所述目标图片集中各图片进行特征提取，得到与所述目标图片集中各图片对应的图片特征向量；
检索结果特征向量获取单元，用于将与所述图片集中各图片对应的图片特征向量均与预先构建的图片库中各图片的特征向量进行皮尔逊相似度计算，获取与所述图片集中各图片对应的图片特征向量的皮尔逊相似度大于预设相似度阈值的特征向量以作为检索结果特征向量；
农作物类别标签获取单元，用于获取所述检索结果特征向量在所述图片库中对应的检索结果图片及检索结果图片对应的农作物类别标签，将检索结果图片对应的农作物类别标签作为所述视频信息中存在的农作物类别。
在本实施例中，为了对视频信息进行图像识别且为了减少数据处理量，可以预先设置起始时间点(如第15秒)和获取时长(如15秒)，之后在所述请求端对应的视频信息中根据起始时间点和获取时长在所述视频信息中获取目标视频段。例如，此时从所述请求端对应的视频信息中由第15秒开始获取了15秒时长的视频作为目标视频段。
之后通过视频拆分获取所述目标视频段中的多帧图片，以组成目标图片集，在获取个目标图片的图片特征向量时，先获取与每一目标图片对应的像素矩阵，然后将每一目标图片对应的像素矩阵作为卷积神经网络模型中输入层的输入，得到多个特征图，之后将特征图输入池化层，得到每一特征图对应的最大值所对应一维向量，最后将每一特征图对应的最大值所对应一维向量输入至全连接层，得到与每一目标图片对应的图片特征向量。
由于图片库中已存储的特征模板中存储了已采集的海量的人图片的特征向量，有了这些海量的特征模板为数据基础后，可以用来确定目标图片对应的农作物类别，从而实现图像识别。
结果发送单元150，用于将所述识别结果发送至所述请求端。
在本实施例中，当服务器完成了对所述音频提取结果的文本提取，将所述识别结果发送至所述请求端，请求端对应的业务员可以根据识别结果中的查勘 方案获取理赔中需要得到的重要参数，从而实现现场查勘。
该装置实现了查勘定损过程中邀请专业人员在线视频以协助定损，提高了查勘定损结果的准确性以及查勘定损效率。
上述基于视频的农作物查勘装置可以实现为计算机程序的形式，该计算机程序可以在如图6所示的计算机设备上运行。
请参阅图6，图6是本申请实施例提供的计算机设备的示意性框图。该计算机设备500是服务器，服务器可以是独立的服务器，也可以是多个服务器组成的服务器集群。
参阅图6，该计算机设备500包括通过系统总线501连接的处理器502、存储器和网络接口505，其中，存储器可以包括非易失性存储介质503和内存储器504。
该非易失性存储介质503可存储操作系统5031和计算机程序5032。该计算机程序5032被执行时，可使得处理器502执行基于视频的农作物查勘方法。
该处理器502用于提供计算和控制能力，支撑整个计算机设备500的运行。
该内存储器504为非易失性存储介质503中的计算机程序5032的运行提供环境，该计算机程序5032被处理器502执行时，可使得处理器502执行基于视频的农作物查勘方法。
该网络接口505用于进行网络通信，如提供数据信息的传输等。本领域技术人员可以理解，图6中示出的结构，仅仅是与本申请方案相关的部分结构的框图，并不构成对本申请方案所应用于其上的计算机设备500的限定，具体的计算机设备500可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
其中，所述处理器502用于运行存储在存储器中的计算机程序5032，以实现本申请实施例中基于视频的农作物查勘方法。
本领域技术人员可以理解，图6中示出的计算机设备的实施例并不构成对计算机设备具体构成的限定，在其他实施例中，计算机设备可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置。例如，在一些实施例中，计算机设备可以仅包括存储器及处理器，在这样的实施例中，存储器及处理器的结构及功能与图6所示实施例一致，在此不再赘述。
应当理解，在本申请实施例中，处理器502可以是中央处理单元(Central  Processing Unit，CPU)，该处理器502还可以是其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。其中，通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
在本申请的另一实施例中提供计算机可读存储介质。该计算机可读存储介质可以为非易失性的计算机可读存储介质。该计算机可读存储介质存储有计算机程序，其中计算机程序被处理器执行时实现本申请实施例中基于视频的农作物查勘方法。
所述存储介质为实体的、非瞬时性的存储介质，例如可以是U盘、移动硬盘、只读存储器(Read-Only Memory，ROM)、磁碟或者光盘等各种可以存储程序代码的实体存储介质。
所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，上述描述的设备、装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到各种等效的修改或替换，这些修改或替换都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以权利要求的保护范围为准。

Claims (20)

  1. 一种基于视频的农作物查勘方法，包括：
    若检测到请求端发出的查勘协助请求指令，获取所述请求端的定位信息；
    获取与所定位信息之间的间距在预设的距离阈值之内的协助端，以组成协助端集合；
    若检测到请求端与协助端集合中一个协助端的视频连接成功指令，获取协助端与请求端之间的视频信息并保存；其中，协助端与请求端之间的视频信息包括协助端对应获取的协助端视频数据、以及请求端对应获取的请求端视频数据；所述请求端视频数据包括农作物视频信息及请求人音频数据；所述协助端视频数据包括协助端音频数据；
    对所述视频信息进行音频提取，得到音频提取结果，通过语音识别模型获取所述音频提取结果的文本以得到识别结果；以及
    将所述识别结果发送至所述请求端。
  2. 根据权利要求1所述的基于视频的农作物查勘方法，其中，所述获取与所定位信息之间的间距在预设的距离阈值之内的协助端，以组成协助端集合之后，还包括：
    获取与所述查勘协助请求对应的请求标签，并获取所述协助端集合各协助端对应的标签与所述请求标签之间的相似度；
    若所述协助端集合各协助端对应的标签中存在与所述请求标签的相似度超出预设的相似度阈值的目标标签，以目标标签组成目标标签集合；
    获取与所述目标标签集合对应的协助端，以得到更新后的协助端集合。
  3. 根据权利要求2所述的基于视频的农作物查勘方法，其中，所述获取所述协助端集合各协助端对应的标签与所述请求标签之间的相似度，包括：
    获取所述协助端集合各协助端对应的标签与所述请求标签之间的字符串编辑距离，以作为所述协助端集合各协助端对应的标签与所述请求标签之间的相似度。
  4. 根据权利要求1所述的基于视频的农作物查勘方法，其中，所述通过语音识别模型获取所述音频提取结果的文本以得到识别结果，包括：
    通过N-gram模型对所述音频提取结果进行识别，以得到识别结果；
    所述对所述视频信息进行音频提取，得到音频提取结果，通过语音识别模型获取所述音频提取结果的文本以得到识别结果之前，还包括：
    接收训练集语料库，将所述训练集语料库输入至初始N-gram模型进行训练，得到N-gram模型；其中，所述N-gram模型为N元模型。
  5. 根据权利要求1所述的基于视频的农作物查勘方法，其中，所述对所述视频信息进行音频提取，得到音频提取结果，通过语音识别模型获取所述音频提取结果的文本以得到识别结果之后，还包括：
    通过图像识别获取所述视频信息中存在的农作物类别，将所述农作物类别作为所述识别结果的属性标签。
  6. 根据权利要求5所述的基于视频的农作物查勘方法，其中，所述通过图像识别获取所述视频信息中存在的农作物类别，包括：
    根据预设的起始时间点和获取时长在所述视频信息中获取目标视频段；
    通过视频拆分获取所述目标视频段中的多帧图片，以组成目标图片集；
    通过卷积神经网络模型对所述目标图片集中各图片进行特征提取，得到与所述目标图片集中各图片对应的图片特征向量；
    将与所述图片集中各图片对应的图片特征向量均与预先构建的图片库中各图片的特征向量进行皮尔逊相似度计算，获取与所述图片集中各图片对应的图片特征向量的皮尔逊相似度大于预设相似度阈值的特征向量以作为检索结果特征向量；
    获取所述检索结果特征向量在所述图片库中对应的检索结果图片及检索结果图片对应的农作物类别标签，将检索结果图片对应的农作物类别标签作为所述视频信息中存在的农作物类别。
  7. 根据权利要求1所述的基于视频的农作物查勘方法，其中，所述获取协助端与请求端之间的视频信息并保存，包括：
    以请求端对应的用户ID、协助端对应的用户ID及当前系统时间生成流水号，以所述流水号为文件名称在存储区域中新建文件夹，将所述获取协助端与请求端之间的视频信息保存至新建文件夹。
  8. 根据权利要求1所述的基于视频的农作物查勘方法，其中，所述对所述视频信息进行音频提取，得到音频提取结果，包括：
    将视频信息中的视频通道信息进行去除，得到音频提取结果。
  9. 一种基于视频的农作物查勘装置，包括：
    定位获取单元，用于若检测到请求端发出的查勘协助请求指令，获取所述请求端的定位信息；
    协助端集合获取单元，用于获取与所定位信息之间的间距在预设的距离阈值之内的协助端，以组成协助端集合；
    视频获取单元，用于若检测到请求端与协助端集合中一个协助端的视频连接成功指令，获取协助端与请求端之间的视频信息并保存；其中，协助端与请求端之间的视频信息包括协助端对应获取的协助端视频数据、以及请求端对应获取的请求端视频数据；所述请求端视频数据包括农作物视频信息及请求人音频数据；所述协助端视频数据包括协助端音频数据；
    音频识别单元，用于对所述视频信息进行音频提取，得到音频提取结果，通过语音识别模型获取所述音频提取结果的文本以得到识别结果；以及
    结果发送单元，用于将所述识别结果发送至所述请求端。
  10. 根据权利要求9所述的基于视频的农作物查勘装置，其中，还包括：
    相似度获取单元，用于获取与所述查勘协助请求对应的请求标签，并获取所述协助端集合各协助端对应的标签与所述请求标签之间的相似度；
    目标标签集合获取单元，用于若所述协助端集合各协助端对应的标签中存在与所述请求标签的相似度超出预设的相似度阈值的目标标签，以目标标签组成目标标签集合；
    集合更新单元，用于获取与所述目标标签集合对应的协助端，以得到更新后的协助端集合。
  11. 一种计算机设备，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现以下步骤：
    若检测到请求端发出的查勘协助请求指令，获取所述请求端的定位信息；
    获取与所定位信息之间的间距在预设的距离阈值之内的协助端，以组成协助端集合；
    若检测到请求端与协助端集合中一个协助端的视频连接成功指令，获取协助端与请求端之间的视频信息并保存；其中，协助端与请求端之间的视频信息包括协助端对应获取的协助端视频数据、以及请求端对应获取的请求端视频数 据；所述请求端视频数据包括农作物视频信息及请求人音频数据；所述协助端视频数据包括协助端音频数据；
    对所述视频信息进行音频提取，得到音频提取结果，通过语音识别模型获取所述音频提取结果的文本以得到识别结果；以及
    将所述识别结果发送至所述请求端。
  12. 根据权利要求11所述的计算机设备，其中，所述获取与所定位信息之间的间距在预设的距离阈值之内的协助端，以组成协助端集合之后，还包括：
    获取与所述查勘协助请求对应的请求标签，并获取所述协助端集合各协助端对应的标签与所述请求标签之间的相似度；
    若所述协助端集合各协助端对应的标签中存在与所述请求标签的相似度超出预设的相似度阈值的目标标签，以目标标签组成目标标签集合；
    获取与所述目标标签集合对应的协助端，以得到更新后的协助端集合。
  13. 根据权利要求12所述的计算机设备，其中，所述获取所述协助端集合各协助端对应的标签与所述请求标签之间的相似度，包括：
    获取所述协助端集合各协助端对应的标签与所述请求标签之间的字符串编辑距离，以作为所述协助端集合各协助端对应的标签与所述请求标签之间的相似度。
  14. 根据权利要求11所述的计算机设备，其中，所述通过语音识别模型获取所述音频提取结果的文本以得到识别结果，包括：
    通过N-gram模型对所述音频提取结果进行识别，以得到识别结果；
    所述对所述视频信息进行音频提取，得到音频提取结果，通过语音识别模型获取所述音频提取结果的文本以得到识别结果之前，还包括：
    接收训练集语料库，将所述训练集语料库输入至初始N-gram模型进行训练，得到N-gram模型；其中，所述N-gram模型为N元模型。
  15. 根据权利要求11所述的计算机设备，其中，所述对所述视频信息进行音频提取，得到音频提取结果，通过语音识别模型获取所述音频提取结果的文本以得到识别结果之后，还包括：
    通过图像识别获取所述视频信息中存在的农作物类别，将所述农作物类别作为所述识别结果的属性标签。
  16. 根据权利要求15所述的计算机设备，其中，所述通过图像识别获取所 述视频信息中存在的农作物类别，包括：
    根据预设的起始时间点和获取时长在所述视频信息中获取目标视频段；
    通过视频拆分获取所述目标视频段中的多帧图片，以组成目标图片集；
    通过卷积神经网络模型对所述目标图片集中各图片进行特征提取，得到与所述目标图片集中各图片对应的图片特征向量；
    将与所述图片集中各图片对应的图片特征向量均与预先构建的图片库中各图片的特征向量进行皮尔逊相似度计算，获取与所述图片集中各图片对应的图片特征向量的皮尔逊相似度大于预设相似度阈值的特征向量以作为检索结果特征向量；
    获取所述检索结果特征向量在所述图片库中对应的检索结果图片及检索结果图片对应的农作物类别标签，将检索结果图片对应的农作物类别标签作为所述视频信息中存在的农作物类别。
  17. 根据权利要求11所述的计算机设备，其中，所述获取协助端与请求端之间的视频信息并保存，包括：
    以请求端对应的用户ID、协助端对应的用户ID及当前系统时间生成流水号，以所述流水号为文件名称在存储区域中新建文件夹，将所述获取协助端与请求端之间的视频信息保存至新建文件夹。
  18. 根据权利要求11所述的计算机设备，其中，所述对所述视频信息进行音频提取，得到音频提取结果，包括：
    将视频信息中的视频通道信息进行去除，得到音频提取结果。
  19. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序当被处理器执行时使所述处理器执行以下操作：
    若检测到请求端发出的查勘协助请求指令，获取所述请求端的定位信息；
    获取与所定位信息之间的间距在预设的距离阈值之内的协助端，以组成协助端集合；
    若检测到请求端与协助端集合中一个协助端的视频连接成功指令，获取协助端与请求端之间的视频信息并保存；其中，协助端与请求端之间的视频信息包括协助端对应获取的协助端视频数据、以及请求端对应获取的请求端视频数据；所述请求端视频数据包括农作物视频信息及请求人音频数据；所述协助端视频数据包括协助端音频数据；
    对所述视频信息进行音频提取，得到音频提取结果，通过语音识别模型获取所述音频提取结果的文本以得到识别结果；以及
    将所述识别结果发送至所述请求端。
  20. 根据权利要求19所述的计算机可读存储介质，其中，所述获取与所定位信息之间的间距在预设的距离阈值之内的协助端，以组成协助端集合之后，还包括：
    获取与所述查勘协助请求对应的请求标签，并获取所述协助端集合各协助端对应的标签与所述请求标签之间的相似度；
    若所述协助端集合各协助端对应的标签中存在与所述请求标签的相似度超出预设的相似度阈值的目标标签，以目标标签组成目标标签集合；
    获取与所述目标标签集合对应的协助端，以得到更新后的协助端集合。
PCT/CN2019/118245 2019-08-15 2019-11-14 基于视频的农作物查勘方法、装置及计算机设备 Ceased WO2021027156A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910752906.1 2019-08-15
CN201910752906.1A CN110688513B (zh) 2019-08-15 2019-08-15 基于视频的农作物查勘方法、装置及计算机设备

Publications (1)

Publication Number Publication Date
WO2021027156A1 true WO2021027156A1 (zh) 2021-02-18

Family

ID=69108260

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2019/118245 Ceased WO2021027156A1 (zh) 2019-08-15 2019-11-14 基于视频的农作物查勘方法、装置及计算机设备

Country Status (2)

Country Link
CN (1) CN110688513B (zh)
WO (1) WO2021027156A1 (zh)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115311103A (zh) * 2022-09-29 2022-11-08 中国太平洋财产保险股份有限公司四川分公司 一种风险评估系统和方法

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111698446B (zh) * 2020-05-26 2021-09-21 上海智勘科技有限公司 在实时视频中同时进行文本信息传输的方法
CN114449206A (zh) * 2020-10-30 2022-05-06 中国移动通信集团设计院有限公司 无人机站点查勘视频拍摄方法、装置及可读存储介质
CN113689430B (zh) * 2021-10-26 2022-02-15 紫东信息科技(苏州)有限公司 肠镜检查状态监测的图像处理方法与装置

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104866956A (zh) * 2015-05-12 2015-08-26 集时通（福建）信息科技有限公司 应用于养殖业和种植业的现场勘险信息获取辅助管理系统
US20160055592A1 (en) * 2014-08-25 2016-02-25 David P. Groeneveld System, Method and Product for Automated Crop Insurance Loss Adjusting for Prevented Planting Conditions
CN105898214A (zh) * 2016-03-24 2016-08-24 北京农业信息技术研究中心 现场查勘装置、现场查勘确认的方法及服务器
CN106303426A (zh) * 2016-08-17 2017-01-04 苏州华兴源创电子科技有限公司 一种车辆远程定损方法及系统
CN107169018A (zh) * 2017-04-06 2017-09-15 河南云保遥感科技有限公司 一种农业保险查勘、定损系统及其实现方法

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8688483B2 (en) * 2013-05-17 2014-04-01 Watts And Associates, Inc. Systems, computer-implemented methods, and computer medium to determine premiums and indemnities for supplemental crop insurance
KR20170084790A (ko) * 2016-01-13 2017-07-21 주식회사 테크인모션 음성 인식 기반의 건강 관리 앱을 실행하는 이동통신 단말기 및 그 동작 방법
CN111914692B (zh) * 2017-04-28 2023-07-14 创新先进技术有限公司 车辆定损图像获取方法及装置
CN107833141A (zh) * 2017-11-27 2018-03-23 无锡同春新能源科技有限公司 基于北斗导航的无人机测定山药田水灾的保险理赔系统
CN107909492A (zh) * 2017-11-30 2018-04-13 河南云保遥感科技有限公司 一种利用机器学习技术的农业保险查勘定损方法
CN108053325A (zh) * 2017-11-30 2018-05-18 河南云保遥感科技有限公司 一种基于农作物遥感技术的农业保险定损方法
CN108769624B (zh) * 2018-07-09 2020-09-22 北京精友世纪软件技术有限公司 一种智能车险移动视频查勘系统
CN109859055A (zh) * 2018-12-13 2019-06-07 中国平安财产保险股份有限公司 保费计算方法、装置、计算机设备及存储介质

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160055592A1 (en) * 2014-08-25 2016-02-25 David P. Groeneveld System, Method and Product for Automated Crop Insurance Loss Adjusting for Prevented Planting Conditions
CN104866956A (zh) * 2015-05-12 2015-08-26 集时通（福建）信息科技有限公司 应用于养殖业和种植业的现场勘险信息获取辅助管理系统
CN105898214A (zh) * 2016-03-24 2016-08-24 北京农业信息技术研究中心 现场查勘装置、现场查勘确认的方法及服务器
CN106303426A (zh) * 2016-08-17 2017-01-04 苏州华兴源创电子科技有限公司 一种车辆远程定损方法及系统
CN107169018A (zh) * 2017-04-06 2017-09-15 河南云保遥感科技有限公司 一种农业保险查勘、定损系统及其实现方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
CHEN, JIANWANG: "Uses of New Technology in Plant Risk Assessment", CHINA INSURANCE, no. 01, 15 January 2018 (2018-01-15), pages 50 - 54, XP055780717 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115311103A (zh) * 2022-09-29 2022-11-08 中国太平洋财产保险股份有限公司四川分公司 一种风险评估系统和方法
CN115311103B (zh) * 2022-09-29 2022-12-30 中国太平洋财产保险股份有限公司四川分公司 一种风险评估系统和方法

Also Published As

Publication number Publication date
CN110688513A (zh) 2020-01-14
CN110688513B (zh) 2023-08-18

Similar Documents

Publication Publication Date Title
CN110046600B (zh) 用于人体检测的方法和装置
WO2021027156A1 (zh) 基于视频的农作物查勘方法、装置及计算机设备
WO2021003819A1 (zh) 基于知识图谱的人机对话方法及人机对话装置
CN108287857B (zh) 表情图片推荐方法及装置
US11544307B2 (en) Personnel selecting device, personnel selecting system, personnel selecting method, and recording medium
US10482169B2 (en) Recommending form fragments
CN105678587A (zh) 一种推荐特征确定方法、信息推荐方法及装置
CN114912629B (zh) 联合感知模型训练、联合感知方法、装置、设备和介质
CN112001370A (zh) 一种农作物病虫害识别方法及系统
CN107870984A (zh) 识别搜索词的意图的方法和装置
CN117332068B (zh) 人机交互方法、装置、电子设备以及存储介质
CN108427941B (zh) 用于生成人脸检测模型的方法、人脸检测方法和装置
CN109993627A (zh) 推荐方法、推荐模型的训练方法、装置和存储介质
CN116894498B (zh) 网络模型的训练方法、策略识别方法、装置以及设备
JP2022185143A (ja) テキスト検出方法、テキスト認識方法及び装置
WO2023065989A1 (zh) 植物病虫害诊断方法、诊断系统及可读存储介质
CN110472143A (zh) 一种信息推送方法、装置、可读存储介质及终端设备
CN116842281A (zh) 兴趣点poi的表征信息获取方法、装置及电子设备
CN111651674B (zh) 双向搜索方法、装置及电子设备
CN120724114A (zh) 内容推荐方法、装置、电子设备、介质及程序产品
CN116259078B (zh) 一种杀虫剂推荐方法、装置、设备及存储介质
CN113807413B (zh) 对象的识别方法、装置、电子设备
CN115761481A (zh) 杂草的识别方法、装置、设备及存储介质
CN111209407B (zh) 数据处理方法、装置、电子设备和计算机可读存储介质
CN115205892A (zh) 流浪宠物寻主方法、装置、电子设备及存储介质

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19941096

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19941096

Country of ref document: EP

Kind code of ref document: A1