CN114154865A - Object evaluation method, device, apparatus, storage medium, and program product - Google Patents

Object evaluation method, device, apparatus, storage medium, and program product Download PDF

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CN114154865A
CN114154865A CN202111472001.2A CN202111472001A CN114154865A CN 114154865 A CN114154865 A CN 114154865A CN 202111472001 A CN202111472001 A CN 202111472001A CN 114154865 A CN114154865 A CN 114154865A
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任怡
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Beijing Baidu Netcom Science and Technology Co Ltd
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Abstract

The present disclosure provides an object evaluation method, apparatus, device, storage medium, and computer program product, which relate to the field of computers, and in particular, to the field of artificial intelligence. The specific implementation scheme is as follows: acquiring information to be evaluated of an object, wherein the information to be evaluated comprises at least one of structural information and environmental information; determining a risk factor evaluation result of the object according to the information to be evaluated; and comparing the information to be evaluated with the reference information of the reference object to determine the risk evaluation result of the object.

Description

Object evaluation method, device, apparatus, storage medium, and program product
Technical Field
The present disclosure relates to the field of computer technology, and more particularly, to the field of artificial intelligence technology.
Background
All aspects of production and life relate to object assessment, and with the development of the internet, higher requirements are put on the accuracy of object assessment.
Disclosure of Invention
The present disclosure provides an object evaluation method, apparatus, device, storage medium, and computer program product.
According to an aspect of the present disclosure, there is provided an object evaluation method, including obtaining information to be evaluated of an object, the information to be evaluated including at least one of structural information and environmental information; determining a risk factor evaluation result of the object according to the information to be evaluated; and comparing the information to be evaluated with the reference information of the reference object to determine the risk evaluation result of the object.
According to another aspect of the present disclosure, there is provided a subject evaluation apparatus including: the system comprises an information to be evaluated acquisition module, a risk factor evaluation result determination module and a risk evaluation result determination module, wherein the information to be evaluated acquisition module is used for acquiring information to be evaluated of an object, the information to be evaluated comprises at least one of structural information and environmental information, the risk factor evaluation result determination module is used for determining a risk factor evaluation result of the object according to the information to be evaluated, and the risk evaluation result determination module is used for comparing the information to be evaluated with reference information of a reference object to determine a risk evaluation result of the object.
According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the embodiments of the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method according to the embodiments of the present disclosure.
According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements a method according to embodiments of the present disclosure.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 illustrates a system architecture of an object evaluation method, apparatus suitable for embodiments of the present disclosure;
FIG. 2 illustrates a flow chart of a subject evaluation method according to an embodiment of the present disclosure;
FIG. 3 schematically illustrates a diagram of a subject evaluation method according to another embodiment of the present disclosure;
FIG. 4 illustrates a schematic diagram of a subject evaluation method according to yet another embodiment of the present disclosure;
FIG. 5 illustrates a schematic diagram of a subject evaluation method according to yet another embodiment of the present disclosure;
FIG. 6 schematically illustrates a method of evaluating a subject according to yet another embodiment of the present disclosure;
FIG. 7 illustrates a block diagram of a subject evaluation apparatus according to an embodiment of the present disclosure;
fig. 8 illustrates a block diagram of an electronic device for implementing the object assessment method of the embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 schematically shows a system architecture of an object evaluation method and apparatus according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, a system architecture 100 according to this embodiment may include clients 101A, 101B, 101C, a network 102, and a server 103. Network 102 is the medium used to provide communication links between clients 101A, 101B, 101C and server 103. Network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
A user may use a client 101A, 101B, 101C to interact with a server 103 over a network 102 to receive or send messages, etc. Various messaging client applications, such as navigation-type applications, web browser applications, search-type applications, instant messaging tools, mailbox clients, social platform software, etc. (examples only) may be installed on the clients 101A, 101B, 101C.
The clients 101A, 101B, 101C may be various electronic devices having display screens and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like. The clients 101A, 101B, 101C of the disclosed embodiments may run applications, for example.
The server 103 may be a server that provides various services, such as a background management server (for example only) that provides support for websites browsed by users using the clients 101A, 101B, 101C. The background management server may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the client. In addition, the server 103 may also be a cloud server, that is, the server 103 has a cloud computing function.
It should be noted that the object evaluation method provided by the embodiment of the present disclosure may be executed by the server 103. Accordingly, the object evaluation apparatus provided by the embodiment of the present disclosure may be disposed in the server 103. The object evaluation method provided by the embodiments of the present disclosure may also be performed by a server or a server cluster that is different from the server 103 and is capable of communicating with the clients 101A, 101B, 101C and/or the server 103. Accordingly, the object evaluation device provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 103 and capable of communicating with the clients 101A, 101B, 101C and/or the server 103.
In one example, the server 103 may obtain information to be evaluated from the clients 101A, 101B, 101C over the network 102.
It should be understood that the number of clients, networks, and servers in FIG. 1 is merely illustrative. There may be any number of clients, networks, and servers, as desired for an implementation.
It should be noted that in the technical solution of the present disclosure, the processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the user are all in accordance with the regulations of the relevant laws and regulations, and do not violate the customs of the public order.
Fig. 2 illustrates a flow chart of a subject evaluation method according to an embodiment of the present disclosure.
As shown in fig. 2, the object evaluation method 200 according to an embodiment of the present disclosure includes operations S210 to S230.
The evaluation of the object may be understood as a process of evaluating some characteristics of the object by using an evaluation method and obtaining an evaluation result. The evaluation results obtained by the evaluation of the object can be used, for example, to aid in the decision-making.
In operation S210, information to be evaluated of the object is acquired.
The information to be evaluated includes at least one of structural information and environmental information.
The information to be evaluated can be understood as basic information for evaluating the object, and the evaluation result of the object can be obtained by analyzing and judging the information to be evaluated by using an evaluation method. For an object, such as a house, the structural information, the environmental information may be the cause of the risk affecting the object.
In operation S220, a risk factor evaluation result of the object is determined according to the information to be evaluated.
In operation S230, the information to be evaluated is compared with the reference information of the reference object, and a risk evaluation result of the object is determined.
The object is referred to as an object to be evaluated, the reference object may be understood as belonging to the same or similar category as the object to be evaluated, the reference information of the reference object may be understood as having the same or similar attribute as the information to be evaluated, and the reference information of the reference object has a meaning for referring to the information to be evaluated for evaluating the object to be evaluated.
The risk factor may be understood as a factor having a correlation with the risk evaluation result of the subject, and the risk factor evaluation result may be understood as an evaluation result of the risk factor of the subject.
The reference object can be predetermined, so that the reference information of the reference object can be more comprehensive and accurate, and the risk assessment result of the object can be accurately determined by comparing the information to be assessed with the reference information of the reference object.
The object evaluation method of the embodiment of the disclosure can accurately evaluate the risk of the object, represent the risk of the object by the risk evaluation result, determine the risk factor evaluation result of the object, and provide a complete evaluation result including both risk factors having relevance to the risk.
The following will exemplify the application of the object evaluation method of the embodiment of the present disclosure to a house object.
The structure information may include, for example, structure information of a house object, and the structure information may include house type information, structure information of a certain position of the house object, and the like, for example, structure information of a balcony position, structure information of a corner position. The environment information may include lighting information, which may be determined by a window opening position, a window opening area, an orientation of the house object, and the like of the house object, and sound information, which may be determined with reference to facilities in the vicinity of the house object, for example, by the position of the house object, whether a station is built in the vicinity of the house object, or whether the house object is close to a railway line or a highway line.
For example, the reference information may be obtained from a related platform in advance, for example, the reference information of the reference house may be obtained from a house renting platform or another platform, and the reference information may include, for example, structure information, environment information, and the like of the reference house, which are not described herein again.
And determining the risk factor evaluation result of the house according to the information to be evaluated. For example, the risk factor evaluation result may include balcony water leakage, and wall corner mold. The risk factor evaluation result of the lighting information may include, for example, insufficient lighting, and the risk factor evaluation result of the sound information may include, for example, decibel high noise.
By referring to the house, a risk assessment result of the house object to be assessed may also be determined, for example, the risk assessment result may be low risk, medium risk, high risk, and the like. Of course, the risk assessment result can also be determined by means of scoring, and the form of the risk assessment result is not limited herein.
Illustratively, the information to be evaluated may further include: comment information of the object, legal information of the object.
The comment information of the object has reference significance for evaluating the risk of the object, and the legal information of the object has reference significance for determining the legality of the object.
The comment information of the house object may include, for example, quality information of the house object provided by other house residents, defect information of the house object, and the legal information of the house object may include, for example, judicial litigation information of the house object.
Fig. 3 schematically shows a schematic diagram of a subject evaluation method according to another embodiment of the present disclosure.
As shown in fig. 3, the object evaluation method 300 according to another embodiment of the present disclosure may further include operation S310. The example method illustrated in fig. 3 may be performed, for example, after operation S220 illustrated in fig. 2, and risk factor overcoming information of the object is determined. It should be understood that, before operation S310, the object assessment method 300 according to the embodiment of the present disclosure may determine the risk factor assessment result 304 according to the information to be assessed 301, and may also obtain the risk assessment result 303 according to the comparison between the information to be assessed 301 and the reference information 302.
In operation S310, risk factor overcoming information 305 of the subject is determined according to the risk factor evaluation result 304.
The risk factor overcoming information may be understood as information for overcoming the risk factor to reduce the risk of the subject.
For example, for each risk factor, information that can overcome the risk factor, that is, information for determining that the risk factor overcomes, may be predetermined, or each risk factor is classified to obtain risk factors of respective categories, and information that can overcome the risk factor of the category, that is, information for determining that the risk factor overcomes, may be predetermined. The corresponding relationship between the risk factors and the risk factor overcoming information can be predetermined, and since the risk factor evaluation result can represent the risk factors, the risk factor overcoming information can be determined according to the corresponding relationship between the risk factors and the risk factor overcoming information after the risk factor evaluation result is determined.
Illustratively, the risk factor evaluation result can also be input into the first evaluation model, and the risk factor overcoming information of the object is determined. Or, the information to be evaluated can be input into the first evaluation model, and the risk factor overcoming information of the object is determined.
Illustratively, the first evaluation model may comprise, for example, a convolutional neural network model or a recursive neural network model.
Convolutional Neural networks, i.e., Convolutional Neural networks, abbreviated CNN. For example, the input of the convolutional neural network model may be a risk factor evaluation result or information to be evaluated, it should be understood that, in the early stage of training the convolutional neural network model, a training sample of a training data set may be input, risk factor overcoming information is output, and after the model reaches a preset precision, the training is completed to obtain a usable model. And inputting the risk factor evaluation result or the information to be evaluated into the trained convolutional neural network model, and outputting corresponding risk factor overcoming information.
Illustratively, the convolutional neural network model may include a Googlenet model.
The training of the recurrent Neural Network, i.e., the recurrent Neural Network, abbreviated as RNN, and the use of the recurrent Neural Network after the training are the same as the principle of the convolutional Neural Network described above, and are not described herein again.
Different inputs of the convolutional neural network model are independent, different outputs are independent, and different from the convolutional neural network model, the recursive neural network model can infer the output at the next moment according to the input at the current moment and is a neural network with time sequence, so that the recursive neural network model can be suitable for risk factor evaluation results or information to be evaluated with time sequence.
The object evaluation method of the embodiment of the disclosure can determine the risk factor overcoming information capable of reducing the risk of the object according to the risk factor evaluation result, can obtain the risk factor evaluation result of the object, the risk evaluation result of the object and the risk factor overcoming information of the object to accurately and completely evaluate the object, and can also reduce the risk of the object by referring to the risk factor overcoming information of the object.
The object evaluation method of the embodiment of the present disclosure is still applied to house object illustration.
For example, the risk factor evaluation result of the house object may include water leakage at a balcony, and the risk factor overcoming information of the house object may include adding a waterproof layer. The risk factor evaluation result of the house object may include a noise decibel high, and the risk factor overcoming information of the house object may include a thickened wall, a door and window installation reinforcing sealing measure, and the like.
Fig. 4 schematically shows a schematic diagram of a risk assessment result of a determined subject in a subject assessment method according to yet another embodiment of the present disclosure.
According to still another embodiment of the present disclosure, a specific example of determining a risk assessment result of a subject in a subject assessment method may be implemented using the following embodiments. Those skilled in the art will appreciate that the example method of fig. 4 may be performed after, for example, operation S220 of fig. 2, to compare the information to be evaluated with the reference information of the reference object, and determine the risk assessment result of the object.
As shown in fig. 4, in operation S410, information to be evaluated 401 is compared with each reference information 402, and a comparison result 403 is obtained.
In order to ensure the accuracy of the risk assessment result, a plurality of reference objects may be preset, and the comparison result obtained by comparing the information to be assessed with the reference information of each reference object may represent, for example, the degree of similarity between the information to be assessed and each reference information.
In operation S420, target reference information 405 is determined according to the comparison result 403, and the target reference information 405 is reference information in which the comparison result 403 is within the reference threshold 404.
It should be understood that the reference information having a higher similarity to the information to be evaluated has a greater reference meaning for determining the risk assessment result of the information to be evaluated. The target reference information may be, for example, reference information having the highest similarity to the information to be evaluated.
In operation S430, a risk assessment result 406 of the information to be assessed is determined according to the reference risk result of the target reference information 405.
For example, the risk result of the reference information of each reference object, that is, the reference risk result, may be determined in advance, the reference risk result may be an actual risk result of the reference object, the reference risk result of the target reference information may be directly used as the risk assessment result of the information to be assessed, or the reference risk result may be referred to with a certain weight to obtain the risk assessment result of the information to be assessed.
Illustratively, a risk assessment result of the information to be assessed may be determined using a K-means clustering model. The determining of the risk assessment result of the information to be assessed according to the K-means clustering model by K-means clustering, namely, K-means clustering algorithm, may include, for example: dividing the reference information into K groups in advance, randomly selecting reference risk results of K reference information as initial values of the mean values, and calculating the distance between each piece of information to be evaluated and each point of the initial mean values. And then classifying the information to be evaluated to a group where the point of the initial mean value closest to the information to be evaluated is located, updating the point of each mean value according to data in the group, and repeating the steps to obtain K groups. Therefore, each piece of information to be evaluated can correspond to the reference risk result of one group as the risk evaluation result of the information to be evaluated.
The object evaluation method of the embodiment of the disclosure can reasonably and accurately determine the target reference information, and can accurately determine the risk evaluation result of the information to be evaluated by referring to the reference risk result of the target reference information.
Fig. 5 schematically shows a schematic view of a risk factor evaluation result of a determined subject in a subject evaluation method according to still another embodiment of the present disclosure.
According to still another embodiment of the present disclosure, a specific example of determining a risk factor evaluation result of a subject in a subject evaluation method may be implemented using the following embodiments. Those skilled in the art will appreciate that the example method shown in fig. 5 may be performed after operation S210 shown in fig. 2, for example, to determine the risk factor evaluation result of the subject according to the information to be evaluated.
As shown in fig. 5, the structural information may include a structural image, and the environmental information may include an environmental image, and in operation S510, the information to be evaluated 501 is input to the risk factor evaluation model 502, and a risk factor evaluation result 503 of the subject is determined.
Illustratively, the risk factor evaluation model may include, for example, a convolutional neural network model or a recursive neural network model. In the above embodiments, the convolutional neural network model and the recursive neural network model have been described in detail, and are not described herein again. Unlike the first evaluation model, the input of the risk factor evaluation model may include information to be evaluated 501, and the output of the risk factor evaluation model may include the risk factor evaluation result of the subject.
In operation S520, according to the risk factor evaluation result 503, the position of the risk factor in the structural image and/or the position of the risk factor in the environmental image are determined.
For example, the position of the risk factor in the image and/or the position of the risk factor in the environmental image may be determined according to the risk factor evaluation model, and the position of the risk factor in the structural image and/or the position of the risk factor in the environmental image may be determined according to the target detection model. The target detection model may include, for example, a YOLO model, the output of which may include a detection box for the risk factors.
According to the object evaluation method, the risk factor evaluation result of the object can be automatically and accurately determined according to the risk factor evaluation model, and the position of the risk factor can be visually determined in the structural image and the environmental image according to the risk factor evaluation result.
FIG. 5 illustrates the location 504 of the risk factor, it being understood that the location 504 of the risk factor may include the location of the risk factor in the structural image and/or the location of the risk factor in the environmental image.
The object evaluation method of the embodiment of the present disclosure is still applied to house object illustration.
For example, the risk factor evaluation model includes a convolutional neural network model, the structural image is an image of a balcony position, the structural image is input into the risk factor evaluation model, the risk factor evaluation model may extract features of the structural image to obtain a feature of water leakage at the balcony position of the house object, and as a risk factor evaluation result of the object, the balcony of the water leakage may be marked in the structural image to determine the position of the risk factor.
Fig. 6 schematically shows a schematic view of a risk factor evaluation result of a determined subject in a subject evaluation method according to still another embodiment of the present disclosure.
According to still another embodiment of the present disclosure, a specific example of determining a risk factor evaluation result of a subject in a subject evaluation method may be implemented using the following embodiments. Those skilled in the art will appreciate that the example method shown in fig. 6 may be performed after operation S210 shown in fig. 2, for example, to determine the risk factor evaluation result of the subject according to the information to be evaluated.
As shown in fig. 6, the object may include a house object, the environment information may include at least one of lighting information 601 and sound information 603, and determining the risk factor evaluation result of the object according to the information to be evaluated may include operations S610 and S620.
In operation S610, the lighting information 601 is processed according to the light source superposition principle, and a risk factor evaluation result 602 of the lighting information is determined.
For example, when the lighting information is two light beams having the same brightness, the superimposed light intensity can be calculated by the following formula (1-1).
Figure BDA0003391378920000101
I1And I2Respectively representing the light intensity of the two beams of light,
Figure BDA0003391378920000102
representing the phase difference of two beams at the observation point, InThe light intensity after superposition is shown, for natural light, the phase of the light wave continuously jumps,
Figure BDA0003391378920000103
the value of (c) will change over time,
Figure BDA0003391378920000104
the time variation is averaged to 0.
In operation S620, the sound information 603 is processed according to the decibel superposition principle, and the risk factor evaluation result 604 of the sound information is determined.
For example, when the sound information is two sound sources, the superimposed decibel value can be calculated using the following equation (1-2).
LGeneral assembly＝10log10(10(L1/10)+10(L2/10)) (1-2)
L1And L2Respectively representing the decibel value, L, of the sound of two sound sources at a certain pointGeneral assemblyAnd expressing the decibel value of the superposed two sound sources.
According to an embodiment of the present disclosure, the present disclosure also provides an object evaluation apparatus.
As shown in fig. 7, a subject evaluation apparatus 700 according to an embodiment of the present disclosure includes: an information to be evaluated acquisition module 710, a risk factor evaluation result determination module 720, and a risk evaluation result determination module 730.
The information to be evaluated obtaining module 710 is configured to obtain information to be evaluated of the object, where the information to be evaluated includes at least one of structure information and environment information. In one embodiment, the information to be evaluated obtaining module 710 may be configured to perform operation S210 described above.
The risk factor evaluation result determining module 720 determines the risk factor evaluation result of the object according to the information to be evaluated. In one embodiment, the risk factor evaluation result determination module 720 may be configured to perform the operation S220 described above.
And a risk assessment result determining module 730, configured to compare the information to be assessed with reference information of a reference object, and determine a risk assessment result of the object. In one embodiment, the risk assessment results determination module 730 may be configured to perform operation S230 described above.
The object assessment apparatus according to the embodiment of the present disclosure may further include a risk factor overcoming information determining module.
And the risk factor overcoming information determining module can be used for determining the risk factor overcoming information of the object according to the risk factor evaluation result.
The object assessment apparatus according to an embodiment of the present disclosure, wherein the risk assessment result determination module may include: the system comprises a comparison submodule, a target reference information determination submodule and a risk assessment result determination submodule.
The comparison submodule can be used for comparing the information to be evaluated with each reference information to obtain a comparison result;
and the target reference information determining submodule can be used for determining target reference information according to the comparison result, wherein the target reference information is reference information of which the comparison result is within the reference threshold range.
And the risk assessment result determining submodule can be used for determining a risk assessment result of the information to be assessed according to the reference risk result of the target reference information.
According to the object evaluation device of the embodiment of the present disclosure, the structural information may include a structural image, the environmental information may include an environmental image, and the risk factor evaluation result determination module may include: a risk factor evaluation result determining submodule and a risk factor position determining submodule.
The risk factor evaluation result determining submodule can be used for inputting the information to be evaluated into the risk factor evaluation model and determining the risk factor evaluation result of the object;
and the risk factor position determining submodule can be used for determining the position of the risk factor in the structural image and/or determining the position of the risk factor in the environmental image according to the risk factor evaluation result.
The subject evaluation apparatus according to an embodiment of the present disclosure, wherein the subject may include a house subject, the environmental information may include at least one of lighting information and sound information, and the risk factor evaluation result determination module may include: a lighting information risk factor evaluation result determining submodule and a decibel information risk factor evaluation result determining submodule.
The lighting information risk factor evaluation result determining submodule can be used for processing lighting information according to a light source superposition principle and determining a risk factor evaluation result of the lighting information;
and the decibel information risk factor evaluation result determining submodule can be used for processing the sound information according to the decibel superposition principle and determining the risk factor evaluation result of the sound information.
According to the object evaluation apparatus of the embodiment of the present disclosure, the information to be evaluated may further include: comment information of the object, legal information of the object.
It should be understood that the embodiments of the apparatus part of the present disclosure are the same as or similar to the embodiments of the method part of the present disclosure, and the technical problems to be solved and the technical effects to be achieved are also the same as or similar to each other, and the detailed description of the present disclosure is omitted.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 8 illustrates a schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 8, the electronic device 800 includes a computing unit 801 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)802 or a computer program loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic apparatus 800 can also be stored. The calculation unit 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
A number of components in the electronic device 800 are connected to the I/O interface 805, including: an input unit 806, such as a keyboard, a mouse, or the like; an output unit 807 such as various types of displays, speakers, and the like; a storage unit 808, such as a magnetic disk, optical disk, or the like; and a communication unit 809 such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
Computing unit 801 may be a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and the like. The calculation unit 801 executes the respective methods and processes described above, such as the object evaluation method. For example, in some embodiments, the subject evaluation method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and/or installed onto device 800 via ROM 802 and/or communications unit 809. When loaded into RAM 803 and executed by the computing unit 801, a computer program may perform one or more of the steps of the object evaluation method described above. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the object evaluation method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server may be a cloud Server, which is also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service extensibility in a traditional physical host and a VPS service ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server incorporating a blockchain.
In the technical scheme of the disclosure, the related record, storage, application and the like of the information to be evaluated are all in accordance with the regulations of related laws and regulations, and do not violate the good customs of the public order.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims (15)

1. A subject evaluation method, comprising:
acquiring information to be evaluated of an object, wherein the information to be evaluated comprises at least one of structural information and environmental information;
determining a risk factor evaluation result of the object according to the information to be evaluated; and
and comparing the information to be evaluated with reference information of a reference object to determine a risk evaluation result of the object.
2. The method of claim 1, further comprising:
and determining risk factor overcoming information of the object according to the risk factor evaluation result.
3. The method of claim 1, wherein comparing the information to be evaluated to reference information of a reference object and determining a risk assessment result of the object comprises:
comparing the information to be evaluated with each piece of reference information to obtain a comparison result;
determining target reference information according to the comparison result, wherein the target reference information is the reference information of the comparison result within a reference threshold range; and
and determining a risk evaluation result of the information to be evaluated according to the reference risk result of the target reference information.
4. The method of claim 1, wherein the structural information comprises a structural image, the environmental information comprises an environmental image, and the determining the risk factor evaluation result of the subject according to the information to be evaluated comprises:
inputting the information to be evaluated into a risk factor evaluation model, and determining the risk factor evaluation result of the object;
and determining the position of the risk factor in the structural image and/or determining the position of the risk factor in the environmental image according to the risk factor evaluation result.
5. The method according to any one of claims 2-4, wherein the object comprises a house object, the environmental information comprises at least one of lighting information and sound information, and the determining the risk factor assessment result of the object according to the information to be assessed comprises:
processing the lighting information according to a light source superposition principle, and determining the risk factor evaluation result of the lighting information;
and processing the sound information according to a decibel superposition principle, and determining the risk factor evaluation result of the sound information.
6. The method of any of claims 1-4, wherein the information to be evaluated further comprises: comment information of the object, legal information of the object.
7. A subject evaluation apparatus comprising:
the device comprises an information to be evaluated acquisition module, a data processing module and a data processing module, wherein the information to be evaluated acquisition module is used for acquiring information to be evaluated of an object, and the information to be evaluated comprises at least one of structural information and environmental information;
a risk factor evaluation result determining module for determining the risk factor evaluation result of the object according to the information to be evaluated; and
and the risk assessment result determining module is used for comparing the information to be assessed with the reference information of the reference object to determine the risk assessment result of the object.
8. The apparatus of claim 7, further comprising:
and the risk factor overcoming information determining module is used for determining the risk factor overcoming information of the object according to the risk factor evaluation result.
9. The apparatus of claim 7, wherein the risk assessment result determination module comprises:
the comparison submodule is used for comparing the information to be evaluated with each piece of reference information to obtain a comparison result;
the target reference information determining submodule is used for determining target reference information according to the comparison result, and the target reference information is reference information of which the comparison result is within a reference threshold range; and
and the risk assessment result determining submodule is used for determining a risk assessment result of the information to be assessed according to a reference risk result of the target reference information.
10. The apparatus of claim 7, the structural information comprising a structural image, the environmental information comprising an environmental image, the risk factor assessment result determination module comprising:
a risk factor evaluation result determining submodule for inputting the information to be evaluated into a risk factor evaluation model and determining the risk factor evaluation result of the object;
and the risk factor position determining submodule is used for determining the position of the risk factor in the structural image and/or determining the position of the risk factor in the environmental image according to the risk factor evaluation result.
11. The apparatus according to any one of claims 8-10, wherein the object comprises a house object, the environmental information comprises at least one of lighting information and sound information, and the risk factor assessment result determination module comprises:
a lighting information risk factor evaluation result determining submodule for processing the lighting information according to a light source superposition principle and determining the risk factor evaluation result of the lighting information;
and the decibel information risk factor evaluation result determining submodule is used for processing the sound information according to a decibel superposition principle and determining the risk factor evaluation result of the sound information.
12. The apparatus of any of claims 7-10, wherein the information to be evaluated further comprises: comment information of the object, legal information of the object.
13. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.
14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.
15. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-6.
CN202111472001.2A 2021-12-03 2021-12-03 Object evaluation method, device, apparatus, storage medium, and program product Pending CN114154865A (en)

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