CN113208595A - Biological expression analysis method, device, storage medium and electronic equipment - Google Patents
Biological expression analysis method, device, storage medium and electronic equipment Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0048—Detecting, measuring or recording by applying mechanical forces or stimuli
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2503/00—Evaluating a particular growth phase or type of persons or animals
- A61B2503/40—Animals
Abstract
The embodiment of the specification relates to the technical field of artificial intelligence, and discloses a biological expression analysis method, a device, a storage medium and electronic equipment.
Description
Technical Field
The present disclosure relates to artificial intelligence technologies, and in particular, to a method, an apparatus, a storage medium, and an electronic device for biological expression analysis.
Background
People and other creatures live together as a global common phenomenon, and many creatures live in the same environment as people and have the function of accompanying, which are generally called as accompanying pets. The existing communication between the companion pet and human generally needs manual work to carry out expression analysis one by one, and the analysis process is complex and long in time consumption.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
An object of the embodiments of the present specification is to provide a method, an apparatus, a storage medium, and an electronic device for analyzing biological expression, which implement analysis of biological expression and improve efficiency and accuracy of human-to-biological communication.
In one aspect, embodiments of the present disclosure provide a method for biological expression analysis, the method including:
collecting characteristic parameters of a living being through a sensing concentrator which is configured in the living being in advance, wherein the characteristic parameters comprise nerve parameters;
converting the neural parameters into corresponding voltage parameters;
reading the voltage parameter by a preconfigured reader;
inputting the voltage parameters into a pre-established biological expression analysis model strategy, and determining expression parameters corresponding to the voltage parameters, wherein the biological expression analysis model strategy comprises the following steps: and the biological expression analysis model component is obtained by training according to the corresponding relation between the historical voltage parameters and the historical expression parameters of a plurality of same-class organisms.
Further, the method further comprises:
and sending the expression parameters to corresponding receiving terminals, so that the receiving terminals determine the state of the living beings according to the expression parameters.
Further, the characteristic parameters further include: a location parameter;
the method further comprises the following steps:
reading the position parameters through a pre-configured interpreter;
correspondingly, the inputting the voltage parameter into a pre-established biological expression analysis model strategy to determine an expression parameter corresponding to the voltage parameter includes:
inputting the voltage parameter and the position parameter into a pre-established biological expression analysis model strategy, and determining expression parameters corresponding to the voltage parameter and the position parameter, wherein the biological expression analysis model strategy comprises the following steps: and the biological expression analysis model component is obtained by training according to the corresponding relation between the historical voltage parameters of a plurality of same-class organisms, the historical position parameters of the same-class organisms and the historical expression parameters.
Further, the location parameters include: direction parameters, motion parameters, air pressure parameters, and magnetic force parameters.
Further, the inputting the voltage parameter and the position parameter into a pre-established biological expression analytical model strategy to determine expression parameters corresponding to the voltage parameter and the position parameter, which includes:
cleaning the voltage parameter and the position parameter to obtain a cleaned voltage parameter and a cleaned position parameter;
inputting the cleaned voltage parameters and the cleaned position parameters into a pre-established biological expression analysis model strategy, and determining expression parameters corresponding to the cleaned voltage parameters and the cleaned position parameters.
Further, the characteristic parameter is a sensor signal acquired by the sensing hub based on behavioral and/or emotional changes of the living being.
Further, the biological expression analysis model component is established according to the following modes:
acquiring historical voltage parameters of a plurality of groups of same-class organisms, historical position parameters of a plurality of same-class organisms and historical expression parameters corresponding to the historical voltage parameters and the historical position parameters;
establishing the biological expression analysis component, wherein the biological expression analysis model component comprises a plurality of model parameters;
and taking the historical voltage parameters and the historical position parameters as input data of the biological expression analysis model component, taking historical expression parameters corresponding to the historical voltage parameters and the historical position parameters as output data of the biological expression analysis model component, and adjusting the model parameters of the biological expression analysis model component until the biological expression analysis model component meets preset requirements.
In another aspect, the present invention provides a biological expression analysis device, including:
an acquisition module configured to perform acquisition of characteristic parameters of a living being through a sensing hub pre-configured in the living being, the characteristic parameters including neural parameters;
a voltage conversion module configured to perform a conversion of the neural parameters into corresponding voltage parameters;
a parameter reading module configured to perform reading of the voltage parameter by a preconfigured interpreter;
an expression parameter determination module configured to perform an input of the voltage parameter to a pre-established biological expression analysis model strategy and determine an expression parameter corresponding to the voltage parameter, wherein the biological expression analysis model strategy includes: and the biological expression analysis model component is obtained by training according to the corresponding relation between the historical voltage parameters and the historical expression parameters of a plurality of same-class organisms.
In another aspect, the present invention provides a computer-readable storage medium, in which at least one instruction or at least one program is stored, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the biological expression analysis method as described above.
In yet another aspect, the present invention provides an electronic device comprising 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, and the at least one processor implements the biological expression analysis method as described above by executing the instructions stored by the memory.
According to the biological expression analysis method, the biological expression analysis device, the storage medium and the electronic equipment, the sensing concentrator pre-configured in the organism is used for collecting the characteristic parameters of the organism, the characteristic parameters are read through the interpreter matched with the sensing concentrator and then converted, the expression parameters corresponding to the voltage parameters are determined according to the voltage parameters by utilizing a biological expression analysis model strategy, and the expression parameters are behavior or emotion parameters which can be understood by human beings, so that the effective communication between the human beings and companion pets can be realized, and the communication efficiency and accuracy are improved.
Drawings
In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without any creative effort.
FIG. 1 is a schematic diagram of an implementation scenario of a biological expression analysis method provided in an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart of a method for biological expression analysis according to an embodiment of the present disclosure;
FIG. 3 is a schematic flow chart of another biological expression analysis method provided in the embodiments of the present disclosure;
FIG. 4 is a schematic view showing the structure of a biological expression analysis device according to an embodiment of the present disclosure;
fig. 5 is a block diagram showing a hardware configuration of a biological expression analysis server in one embodiment of the present specification.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present specification, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only a part of the embodiments of the present specification, and not all of the embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments in the present specification without any inventive step should fall within the scope of protection of the present specification.
With the development of computer technology, some countries have introduced a chip for embedding pets, and the scan chip can display the serial number of the pet, record the name, address, contact information of the owner, the name, type, sex, hair color, birth year and month of the pet, and the name and address of the veterinarian, so as to prevent the pet from being lost.
On the basis of the above chip, fig. 1 is a schematic view of an implementation scenario of a biological expression analysis method provided in an embodiment of the present disclosure, and as shown in fig. 1, a biological expression analysis system is provided, which may include: the sensing concentrator, the interpreter and the receiving terminal are connected in sequence, the sensing concentrator is arranged in a living body and used for collecting characteristic parameters of the living body, the characteristic parameters collected by the sensing concentrator are interpreted and analyzed through the matched interpreter, and expression parameters after interpretation are sent to the receiving terminal through a communication device in the interpreter, so that the receiving terminal can know the expression of the living body.
The embodiment of the specification provides a biological expression analysis method, which can acquire corresponding neural parameters when the emotion or behavior of a pet changes, and convert the neural parameters to obtain expression parameters which can be read by human beings, so that the human beings can read the emotional expression of the pet, and the efficiency and accuracy of communication between the human beings and the pet are improved.
FIG. 2 is a flow chart of a biological expression analysis method provided in the embodiments of the present specification, and although the present specification provides the method operation steps or device structure shown in the following embodiments or drawings, more or less operation steps or module units after partial combination may be included in the method or device based on the conventional or non-creative work. In the case of steps or structures which do not logically have the necessary cause and effect relationship, the execution order of the steps or the block structure of the apparatus is not limited to the execution order or the block structure shown in the embodiments or the drawings of the present specification. The method or module structure, when applied to a device, server, or end product in practice, may be executed sequentially or in parallel according to embodiments or the method or module structure shown in the drawings (e.g., a parallel processor or multi-thread processing environment, or even an implementation environment including distributed processing and server clustering).
In a specific embodiment, as shown in fig. 2, in an embodiment of the biological expression analysis method provided in this specification, the method may be applied to the biological expression analysis system, where the receiving end may include a server, a computer, a smart phone, a tablet computer, a smart phone, and other devices, and the method may include the following steps:
In a specific implementation, the sensing hub is arranged in a living body, and the sensing hub can be provided with a plurality of nerve sensors for collecting characteristic parameters of pets. Such as: by receiving the change of the nerve conduction potential through the electrode receiver, it can be understood that the nerve sensor can be connected with the nervous system of the implanted pet, and when the emotion or behavior of the pet changes, the parameter of the neural expression changes along with the change, so that the generated voltage value changes. A nerve is a structure in the peripheral nervous system consisting of nerve fibers grouped in bundles, one or more nerve fiber bundles can be contained in one nerve, the nerve is externally wrapped by the adventitia and internally separated by the fasciculi. The nerve fiber itself refers to the axon of the neuron, and the outside of the axon is covered by the myelin sheath formed by glial cells, thus playing roles of protection, insulation and the like. It is understood that the characteristic parameter is a sensor signal acquired by the sensing hub based on behavioral and/or emotional changes of the living being.
And 104, converting the neural parameters into corresponding voltage parameters.
In a specific implementation process, when the behavior or emotion of a living being changes, nerve impulses are generated, and the conduction process of the nerve impulses is an electrochemical process and is an electrochemical change sequentially generated on nerve fibers. When nerves are stimulated, the permeability of cell membranes changes dramatically. The nerve parameters of the living beings can be converted into corresponding voltage parameters through the arranged sensing hub, namely, different voltage parameters can represent different behaviors or emotions of the living beings. It will be appreciated that the voltage parameter collected by the sensing hub is continuous, i.e., the voltage parameter may be a continuous voltage fluctuation curve.
And step 106, reading the voltage parameter through a pre-configured interpreter.
In a specific implementation process, the interpreter and the sensing hub can realize data transmission in a near field communication mode and the like, namely the interpreter can read voltage parameters collected by the sensing hub. The interpreter can also clean the read voltage parameters to obtain corresponding cleaned voltage parameters. It is understood that the voltage parameter collected by the sensing hub is continuous, that is, the voltage parameter may be a continuous voltage fluctuation curve, the interpreter may filter the voltage fluctuation curve to obtain a corresponding voltage parameter, and the voltage parameter after cleaning may be a voltage point value. For example, the interpreter may take maximum and minimum values in the voltage parameter for characterizing behavioral or emotional changes in the living being.
In a specific implementation process, the voltage parameters may be input into a pre-established biological expression analytical model component to obtain corresponding expression parameters. It is understood that the expression parameters may be human-readable parameters.
Illustratively, the Resting Potential (RP) of a pet at rest is 20 mV. When the pet receives external stimulation to generate fear, the nerve signal changes to trigger nerve impulse, so that the potential is increased to 40 mV. The voltage parameter of 40 mV is input into a pre-established biological expression analysis model component, so that the current emotional state of the pet can be determined to be fear.
The conduction process of nerve impulses is an electrochemical process, and is an electrochemical change that occurs sequentially on nerve fibers. When nerves are stimulated, the permeability of cell membranes changes dramatically. Experiments with isotopically labeled ions demonstrated that nerve fibers, when stimulated (e.g., electrically stimulated), had a 20-fold increase in Na + influx and a 9-fold increase in K + efflux relative to their unstimulated counterparts, and therefore nerve impulses were associated with a large influx of Na + and a large efflux of K +.
The resting potential is the potential difference between the positive outside and the negative inside existing on the inner and outer sides of the cell membrane when the cell is not stimulated. It is the basis for all bioelectricity generation and changes. When a pair of measuring microelectrodes are both located outside the film, there is no potential difference between the electrodes. At the moment when the tip of a microelectrode penetrates into the membrane, a sudden potential change is shown on an oscilloscope, which indicates that a potential difference exists between the two electrodes, namely the two sides of the cell membrane, and the potential in the membrane is lower than that outside the membrane. This potential is always constant in a quiet state, and is therefore referred to as a resting potential. The resting potential of almost all animal and plant cells is lower in the membrane than outside the membrane, and if the potential outside the membrane is zero, the potential inside the membrane is a negative value. The resting potential of most cells is-10 to 100 mV.
On the basis of the above embodiments, in an embodiment of the present specification, fig. 3 is a schematic flow chart of another biological expression analysis method provided in the embodiment of the present specification, and as shown in fig. 3, the method further includes:
and step 110, sending the expression parameters to corresponding receiving ends, so that the receiving ends determine the state of the living beings according to the expression parameters.
In a specific implementation process, the interpreter may be provided with a communication device corresponding to the receiving end, and after the interpreter outputs the expression parameters, the expression parameters may be sent to the receiving end. It can be understood that the interpreter and the receiving end may have a one-to-one correspondence, or one receiving end corresponds to multiple receiving ends.
According to the biological expression analysis method provided by the specification, the sensing concentrator pre-configured in a living body is used for collecting the characteristic parameters of the living body, the characteristic parameters are read through the interpreter matched with the sensing concentrator, then the characteristic parameters are converted, the expression parameters corresponding to the voltage parameters are determined according to the voltage parameters by using a biological expression analysis model strategy, the expression parameters are behavior or emotion parameters which can be understood by human, the effective communication between the human and companion pets can be realized, and the communication efficiency and accuracy are improved.
It should be noted that the sensing hub may further be provided with an equipment identifier, the equipment identifier may be obtained when the interpreter reads the information, the generated expression parameter may also carry the equipment identifier, the equipment identifier represents the serial number of the pet, and information such as the name, address, contact information of the owner, the name, type, sex, hair color, birth year and month of the pet, and the name and address of the veterinarian is recorded, so as to prevent the pet from being lost.
On the basis of the above embodiments, in an embodiment of the present specification, the characteristic parameters further include: a location parameter;
the method further comprises the following steps:
step 302, reading the position parameter through a pre-configured interpreter.
In particular implementations, the location parameter may be used to characterize where the sensing hub (i.e., the pet) is located. The location parameters may include: direction parameters, motion parameters, air pressure parameters, magnetic force parameters and the like.
The orientation parameter may be obtained by a gyroscope (gyroscope) disposed in the sensing hub, and the gyroscope may be a device for sensing and maintaining the orientation. The gyroscope is mainly composed of a rotor which is positioned at an axis and can rotate. Due to the angular momentum of the rotor, the gyroscope tends to resist the change in direction once it begins to rotate. It can be understood that the gyroscope can be provided with an initial three-dimensional x-y-z axis when being installed, the directions represented by the three x-y-z axes are different according to different installation accelerometers, and the direction parameters of the gyroscope in the previous period can be compared after the gyroscope rotates, so that the direction parameters representing the direction changes can be obtained.
The motion parameters may be obtained by an accelerometer integrated with the sensor hub, the accelerometer may measure acceleration caused by movement of the sensor hub, and it is understood that the motion parameters may have corresponding directions, such as directions determined using three axes including x-y-z.
The air pressure parameter may be measured by an instrument for measuring air pressure, such as an air pressure gauge or barometer, integrated in the sensing hub. The barometer may record the time-based change in barometric pressure for an area graphically or electronically.
The magnetic force parameters can be obtained through magnetometers integrated on the sensing concentrator, and the magnetometers can be used for measuring included angles between the sensing concentrator and the four directions of the south, the east, the west and the north of the world.
Correspondingly, the inputting the voltage parameter into a pre-established biological expression analysis model strategy to determine an expression parameter corresponding to the voltage parameter includes:
inputting the voltage parameter and the position parameter into a pre-established biological expression analysis model strategy, and determining expression parameters corresponding to the voltage parameter and the position parameter, wherein the biological expression analysis model strategy comprises the following steps: and the biological expression analysis model component is obtained by training according to the corresponding relation between the historical voltage parameters of a plurality of same-class organisms, the historical position parameters of the same-class organisms and the historical expression parameters.
For example, the biological expression analysis model strategy can determine the position of a city according to the air pressure parameter, determine the specific position of the city according to the magnetometer, and determine the relevant parameters of the movement of the pet according to the direction parameter and the movement parameter.
The embodiment of the specification integrates the relevant sensors on the sensing concentrator, so that the motion parameters and the environment parameters of the pet can be acquired, the corresponding expression parameters are determined according to the voltage parameters and the position parameters, the state of the pet can be effectively acquired, and the accuracy of judging the pet expression parameters is improved.
On the basis of the above embodiment, in an embodiment of the present specification, the inputting the voltage parameter and the position parameter into a pre-established biological expression analysis model strategy, and determining the expression parameters corresponding to the voltage parameter and the position parameter, previously include:
cleaning the voltage parameter and the position parameter to obtain a cleaned voltage parameter and a cleaned position parameter;
inputting the cleaned voltage parameters and the cleaned position parameters into a pre-established biological expression analysis model strategy, and determining expression parameters corresponding to the cleaned voltage parameters and the cleaned position parameters.
In a specific implementation process, because redundant parameters exist in the acquired voltage parameters and position parameters, the voltage parameters and the position parameters can be cleaned, and the redundant parameters are filtered out to obtain the cleaned voltage parameters and the cleaned position parameters. The specific cleaning manner is not specifically limited in the embodiments of the present specification, and may be set according to actual needs.
Based on the above embodiment, in one embodiment of the present specification, the characteristic parameter is a sensor signal acquired by the sensing hub based on behavior and/or emotion change of the living being.
On the basis of the above embodiment, in one embodiment of the present specification, the biological expression analysis model component is established according to the following manner:
acquiring historical voltage parameters of a plurality of groups of same-class organisms, historical position parameters of a plurality of same-class organisms and historical expression parameters corresponding to the historical voltage parameters and the historical position parameters;
establishing the biological expression analysis component, wherein the biological expression analysis model component comprises a plurality of model parameters;
and taking the historical voltage parameters and the historical position parameters as input data of the biological expression analysis model component, taking historical expression parameters corresponding to the historical voltage parameters and the historical position parameters as output data of the biological expression analysis model component, and adjusting the model parameters of the biological expression analysis model component until the biological expression analysis model component meets preset requirements.
In a specific implementation process, a biological expression analysis model component may be established, the biological expression analysis model component may include a plurality of model parameters, and the model parameters may represent constraint conditions, and may be specifically set according to expert experience and the like. Model training may be performed on the biological expression analysis component using historical voltage parameters and historical position parameters. Taking the historical voltage parameter and the historical position parameter as the input of the biological expression analysis component, taking the historical expression parameter corresponding to the historical voltage parameter and the historical position parameter as the output of the biological expression analysis component, and continuously adjusting the model parameter in the biological expression analysis component until the biological expression analysis component reaches the preset requirement, such as: and if the preset precision is met and the model parameter adjustment times meet the preset times requirement, completing model training, and specifically referring to a machine learning algorithm such as: a GBDT (Gradient Boosting Decision Tree) algorithm and the like.
Model training is carried out based on historical voltage parameters and historical position parameters, the contents with inconspicuous voltage parameters and position parameters can be identified by constructing a biological expression analysis component, and the accuracy of biological expression analysis can be improved.
After the current voltage parameter and the current position parameter are input into the trained biological expression analysis component, expression parameters of characteristic behaviors such as eating, defecation and the like can be output, or expression parameters of characteristic emotions such as fear, joy, calm and the like can be output.
An embodiment of the present specification further provides a biological expression analysis device, fig. 4 is a schematic structural diagram of the biological expression analysis device in an embodiment of the present specification, and as shown in fig. 4, the device includes:
an acquisition module 61 configured to perform acquisition of characteristic parameters of a living being through a sensing hub pre-configured in the living being, the characteristic parameters including neural parameters;
a voltage conversion module 62 configured to perform a conversion of the neural parameters into corresponding voltage parameters;
a parameter reading module 63 configured to perform reading of the voltage parameter by a preconfigured interpreter;
an expression parameter determination module 64 configured to perform an input of the voltage parameter to a pre-established biological expression analysis model strategy, and determine an expression parameter corresponding to the voltage parameter, wherein the biological expression analysis model strategy includes: and the biological expression analysis model component is obtained by training according to the corresponding relation between the historical voltage parameters and the historical expression parameters of a plurality of same-class organisms.
It should be noted that the above-mentioned apparatuses may also include other embodiments according to the description of the corresponding method embodiments. The specific implementation manner may refer to the description of the above corresponding method embodiment, and is not described in detail herein.
The present specification further provides a computer-readable storage medium, where at least one instruction or at least one program is stored, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the service data testing method in the foregoing embodiments, such as:
collecting characteristic parameters of a living being through a sensing concentrator which is configured in the living being in advance, wherein the characteristic parameters comprise nerve parameters;
converting the neural parameters into corresponding voltage parameters;
reading the voltage parameter by a preconfigured reader;
inputting the voltage parameters into a pre-established biological expression analysis model strategy, and determining expression parameters corresponding to the voltage parameters, wherein the biological expression analysis model strategy comprises the following steps: and the biological expression analysis model component is obtained by training according to the corresponding relation between the historical voltage parameters and the historical expression parameters of a plurality of same-class organisms.
An embodiment of the present specification further provides a service data testing device, where the device includes at least one processor and a memory for storing executable instructions of the processor, and the instructions, when executed by the processor, implement a service data testing method including the foregoing embodiments, where:
collecting characteristic parameters of a living being through a sensing concentrator which is configured in the living being in advance, wherein the characteristic parameters comprise nerve parameters;
converting the neural parameters into corresponding voltage parameters;
reading the voltage parameter by a preconfigured reader;
inputting the voltage parameters into a pre-established biological expression analysis model strategy, and determining expression parameters corresponding to the voltage parameters, wherein the biological expression analysis model strategy comprises the following steps: and the biological expression analysis model component is obtained by training according to the corresponding relation between the historical voltage parameters and the historical expression parameters of a plurality of same-class organisms.
It should be noted that the above description of the apparatus according to the method embodiment may also include other embodiments. The specific implementation manner may refer to the description of the related method embodiment, and is not described in detail herein.
The method or apparatus of the foregoing embodiments provided in this specification can implement service logic through a computer program and record the service logic on a storage medium, and the storage medium can be read and executed by a computer, so as to implement the effects of the solutions described in the embodiments of this specification.
The method embodiments provided by the embodiments of the present specification can be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Taking the example of the operation on the server, fig. 5 is a block diagram of the hardware structure of the biological expression analysis server in an embodiment of the present specification, and the computer terminal may be the biological expression analysis server or the service data testing and processing device in the above embodiment. As shown in fig. 5, the server 10 may include one or more (only one shown) processors 100 (the processors 100 may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, etc.), a non-volatile memory 200 for storing data, and a transmission module 300 for communication functions. It will be understood by those skilled in the art that the structure shown in fig. 5 is only an illustration and is not intended to limit the structure of the electronic device. For example, the server 10 may also include more or fewer components than shown in FIG. 5, and may also include other processing hardware, such as a database or multi-level cache, a GPU, or have a different configuration than shown in FIG. 5, for example.
The non-volatile memory 200 may be configured to store software programs and modules of application software, such as program instructions/modules corresponding to the taxi taking data processing method in the embodiment of the present specification, and the processor 100 executes various functional applications and resource data updates by running the software programs and modules stored in the non-volatile memory 200. Non-volatile memory 200 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the non-volatile memory 200 may further include memory located remotely from the processor 100, which may be connected to a computer terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, office-to-network, mobile communication networks, and combinations thereof.
The transmission module 300 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal. In one example, the transmission module 300 includes a Network adapter (NIC) that can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission module 300 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The service data testing method or apparatus provided in the embodiment of the present specification may be implemented by a processor executing corresponding program instructions in a computer, for example, implemented by using a c + + language of a windows operating system at a PC end, a linux system, or implemented by using android and iOS system programming languages, for example, at an intelligent terminal, and implemented by using processing logic based on a quantum computer.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class embodiment, since it is substantially similar to the method embodiment, the description is simple, and the relevant points can be referred to only the partial description of the method embodiment.
Although one or more embodiments of the present description provide method operational steps as in the embodiments or flowcharts, more or fewer operational steps may be included based on conventional or non-inventive approaches. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When the device or the end product in practice executes, it can execute sequentially or in parallel according to the method shown in the embodiment or the figures (for example, in the environment of parallel processors or multi-thread processing, even in the environment of distributed resource data update). The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises an element is not excluded. The terms first, second, etc. are used to denote names, but not any particular order.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, when implementing one or more of the present description, the functions of each module may be implemented in one or more software and/or hardware, or a module implementing the same function may be implemented by a combination of multiple sub-modules or sub-units, etc. The above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit is merely a division of one logic function, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, and the relevant points can be referred to only part of the description of the method embodiments. In the description of the specification, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
The above description is merely exemplary of one or more embodiments of the present disclosure and is not intended to limit the scope of one or more embodiments of the present disclosure. Various modifications and alterations to one or more embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement or the like made within the spirit and principle of the present specification should be included in the scope of the claims.
Claims (10)
1. A method for biological expression analysis, comprising:
collecting characteristic parameters of a living being through a sensing concentrator which is configured in the living being in advance, wherein the characteristic parameters comprise nerve parameters;
converting the neural parameters into corresponding voltage parameters;
reading the voltage parameter by a preconfigured reader;
inputting the voltage parameters into a pre-established biological expression analysis model strategy, and determining expression parameters corresponding to the voltage parameters, wherein the biological expression analysis model strategy comprises the following steps: and the biological expression analysis model component is obtained by training according to the corresponding relation between the historical voltage parameters and the historical expression parameters of a plurality of same-class organisms.
2. The method for analyzing biological expression according to claim 1, further comprising:
and sending the expression parameters to corresponding receiving terminals, so that the receiving terminals determine the state of the living beings according to the expression parameters.
3. The biological expression analysis method according to claim 1, wherein the characteristic parameters further include: a location parameter;
the method further comprises the following steps:
reading the position parameters through a pre-configured interpreter;
correspondingly, the inputting the voltage parameter into a pre-established biological expression analysis model strategy to determine an expression parameter corresponding to the voltage parameter includes:
inputting the voltage parameter and the position parameter into a pre-established biological expression analysis model strategy, and determining expression parameters corresponding to the voltage parameter and the position parameter, wherein the biological expression analysis model strategy comprises the following steps: and the biological expression analysis model component is obtained by training according to the corresponding relation between the historical voltage parameters of a plurality of same-class organisms, the historical position parameters of the same-class organisms and the historical expression parameters.
4. The biological expression analysis method according to claim 3, wherein the position parameter includes: direction parameters, motion parameters, air pressure parameters, and magnetic force parameters.
5. The method of claim 3, wherein the inputting the voltage parameter and the position parameter into a pre-established biological expression analysis model strategy to determine expression parameters corresponding to the voltage parameter and the position parameter comprises:
cleaning the voltage parameter and the position parameter to obtain a cleaned voltage parameter and a cleaned position parameter;
inputting the cleaned voltage parameters and the cleaned position parameters into a pre-established biological expression analysis model strategy, and determining expression parameters corresponding to the cleaned voltage parameters and the cleaned position parameters.
6. The method according to claim 1, wherein the characteristic parameter is a sensor signal collected by the sensing hub based on a behavior and/or emotion change of the living being.
7. The method according to claim 5, wherein the biological expression analysis model component is established as follows:
acquiring historical voltage parameters of a plurality of groups of same-class organisms, historical position parameters of a plurality of same-class organisms and historical expression parameters corresponding to the historical voltage parameters and the historical position parameters;
establishing the biological expression analysis component, wherein the biological expression analysis model component comprises a plurality of model parameters;
and taking the historical voltage parameters and the historical position parameters as input data of the biological expression analysis model component, taking historical expression parameters corresponding to the historical voltage parameters and the historical position parameters as output data of the biological expression analysis model component, and adjusting the model parameters of the biological expression analysis model component until the biological expression analysis model component meets preset requirements.
8. An apparatus for biological expression analysis, the apparatus comprising:
an acquisition module (61) configured to perform acquisition of characteristic parameters of a living being by a sensing hub pre-configured within the living being, the characteristic parameters including neural parameters;
a voltage conversion module (62) configured to perform a conversion of the neural parameter into a corresponding voltage parameter;
a parameter reading module (63) configured to perform reading of the voltage parameter by a preconfigured interpreter;
an expression parameter determination module (64) configured to perform an input of the voltage parameter to a pre-established biological expression analytical model strategy, and determine an expression parameter corresponding to the voltage parameter, wherein the biological expression analytical model strategy comprises: and the biological expression analysis model component is obtained by training according to the corresponding relation between the historical voltage parameters and the historical expression parameters of a plurality of same-class organisms.
9. A computer-readable storage medium having at least one instruction or at least one program stored therein, the at least one instruction or the at least one program being loaded and executed by a processor to implement the biological expression profiling method according to any one of claims 1 to 7.
10. An electronic device comprising 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, the at least one processor implementing the biological expression parsing method of any one of claims 1-7 by executing the instructions stored by the memory.
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