CN108307546B - Method and device for determining target residence state and electronic equipment - Google Patents

Method and device for determining target residence state and electronic equipment Download PDF

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CN108307546B
CN108307546B CN201710762203.8A CN201710762203A CN108307546B CN 108307546 B CN108307546 B CN 108307546B CN 201710762203 A CN201710762203 A CN 201710762203A CN 108307546 B CN108307546 B CN 108307546B
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余建国
林尚静
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Beijing University of Posts and Telecommunications
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Beijing University of Posts and Telecommunications
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Abstract

The embodiment of the invention provides a method and a device for determining a target residence state and electronic equipment, wherein the method comprises the following steps: aiming at each influence factor influencing the determination of the residence state, determining a first importance degree of the influence factor relative to other influence factors according to a preset comparison scale table, and acquiring a first weight vector; aiming at each influence factor, under the condition that the metric value of the current influence factor is the lowest, determining the second importance degree of each preset resident state of the equipment resident relative to other resident states according to a preset comparison scale table, and acquiring a second weight vector; and determining a third weight vector according to the product of the first weight vector and the second weight vector, and determining the residence state corresponding to the weight factor in the third weight vector as the target residence state. By applying the embodiment of the invention, the target residence state can be determined.

Description

Method and device for determining target residence state and electronic equipment
Technical Field
The invention relates to the technical field of internet of things, in particular to a method and a device for determining a target residence state and electronic equipment.
Background
With the progress of the technical level of the human industry, the birth of the internet of things based on the internet technology is promoted. The Internet of things is a network formed by connecting equipment based on the Internet technology, and has the characteristics of terminal diversification, perception automation and decision intelligence. Each device in the Internet of things is provided with a sensor, the devices can be identified through the sensors, and information exchange is carried out through the Internet, so that information communication between the devices is realized.
The internet of things can comprise a large number of various devices, if parts of the internet of things arranged on each device are always in a connection state, the power consumption of the internet of things can be increased, and the connection state is that the devices are always in a state of being capable of interacting with other devices at any time. In order to reduce the power consumption of the internet of things system, an RRC (Radio Resource Control) technology based on a Long Term Evolution (LTE) system is developed, and each device in the internet of things is divided into different residence states, such as an IDLE state (IDLE state), a DORMANT state (sleep state), a SUSPENDED state (suspend state), and an eDRX state (enhanced Discontinuous Reception state). The stay state is a wireless connection state in which the device is in for a long time. In practical applications, different services of the same device may have different camping preferences for the status, that is, the determining of the camping status is not as important as the influencing factors such as power consumption, signaling overhead, and delay consumption. For example, for a power distribution automation type device, if the device carries an intelligent meter reading service, and the service has low requirements on signaling overhead and delay consumption, it is determined that the influencing factor of the residence state of the device is power consumption. The device may reside in the DORMANT state or eDRX state for low power operation purposes. However, if the device carries a service of the "remote control" type, which has a high requirement on access delay consumption, it is determined that the influencing factor of the device residence state is access delay consumption. If the device still resides in the state of the domant and the eDRX, the device cannot communicate in time when there is a service demand, and then the "remote control" service cannot be implemented well. Thus, the device may be left in the SUSPENDED state for the purpose of low access latency consumption.
By applying the prior art, when the service borne by the equipment is complex, the resident state of the equipment in the internet of things is determined only from one of the influencing factors, and the determination is not accurate enough.
Disclosure of Invention
The embodiment of the invention aims to provide a method and a device for determining a target residence state and electronic equipment, so as to achieve the purpose of accurately determining the residence state of equipment in the Internet of things. The specific technical scheme is as follows:
in order to achieve the above object, an embodiment of the present invention provides a method for determining a target parking status, where the method includes:
aiming at each influence factor influencing the determination of the residence state, determining a first importance degree of each influence factor relative to other influence factors except the current influence factor according to a preset comparison scale table, and acquiring a first weight vector, wherein each influence factor comprises: at least two of power consumption, signaling overhead, and latency consumption;
for each influence factor, under the condition that the metric value of the current influence factor is lowest, determining a second importance degree of each preset resident state of the equipment resident relative to other resident states except the current influence factor according to the preset comparison metric table, and acquiring a second weight vector;
and taking the product of the first weight vector and the second weight vector as a third weight vector, and taking the residence state corresponding to the weight factor in the third weight vector as a target residence state.
Optionally, the taking the residence state corresponding to the weight factor in the third weight vector as the target residence state includes:
and determining the residence state corresponding to the maximum weight factor in the third weight vector as a target residence state.
Optionally, the determining, according to a preset comparison scale table, a first importance degree of each influence factor relative to other influence factors except the current influence factor for each influence factor determined by the influence residence state, and obtaining a first weight vector includes:
aiming at each influence factor influencing the determination of the residence state, acquiring a first importance degree of the influence factor relative to other influence factors except the current influence factor according to a preset comparison scale table, and constructing a first comparison matrix according to the first importance degree;
determining a first random consistency factor corresponding to the order of the first comparison matrix according to the order of the first comparison matrix and the corresponding relation between the order of the first comparison matrix and the first random consistency factor;
according to the maximum eigenvalue of the first comparison matrix and the order of the first comparison matrix, utilizing a formula
Figure GDA0002457268900000031
Calculating a consistency factor for the first comparison matrix, wherein,
CIAa consistency factor being a first comparison matrix; lambda [ alpha ]AIs the maximum eigenvalue of the first contrast matrix; n is the order of the first contrast matrix;
using a formula based on the consistency factor of the first contrast matrix and the first random consistency factor
Figure GDA0002457268900000032
Computing inspection coefficients for the first comparison matrix, wherein,
βAthe checking coefficient of the first comparison matrix; CIAA consistency factor being a first comparison matrix; RI (Ri)AIs a first random consistency factor;
under the condition that the checking coefficient of the first comparison matrix is not smaller than a first preset threshold value, re-determining the first importance degree of the influence factor relative to other influence factors, constructing a second comparison matrix according to the re-determined first importance degree, replacing the content of the first comparison matrix with the content of the second comparison matrix, and returning to execute the step of calculating the consistency factor of the first comparison matrix according to the maximum characteristic value of the first comparison matrix and the order of the first comparison matrix;
and under the condition that the checking coefficient of the first comparison matrix is smaller than a first preset threshold value, acquiring a first weight vector of the first comparison matrix according to the eigenvector of the first comparison matrix and the maximum eigenvalue of the first comparison matrix.
Optionally, the determining, for each influence factor, a second importance degree of each preset residing state of the device with respect to other residing states except the current influence factor according to a preset comparison scale table under the condition that the metric value of the current influence factor is lowest, and acquiring a second weight vector, includes:
for each influence factor, under the condition that the metric value of the current influence factor is lowest, determining a second importance degree of each resident state of the equipment resident relative to other resident states except the current resident state according to a preset comparison scale table, and constructing a third comparison matrix according to the second importance degree;
determining a second random consistency factor corresponding to the order of the third contrast matrix according to the order of the third contrast matrix and the corresponding relation between the order of the third contrast matrix and the second random consistency factor;
according to the maximum eigenvalue of the third contrast matrix and the order of the third contrast matrix, utilizing a formula
Figure GDA0002457268900000041
Calculating a consistency factor for the third contrast matrix, wherein,
CIBis the consistency factor of the third contrast matrix; lambda [ alpha ]BIs the maximum eigenvalue of the third contrast matrix; m is the order of the third contrast matrix;
according to the consistency factor of the third contrast matrix and the second random consistency factor, utilizing a formula
Figure GDA0002457268900000042
Computing inspection coefficients for the third contrast matrix, wherein,
βBthe checking coefficient of the third contrast matrix is; CIBIs the consistency factor of the third contrast matrix; RI (Ri)BIs a second random consistency factor;
under the condition that the checking coefficient of the third contrast matrix is not smaller than a second preset threshold value, re-determining a second importance degree of the influence factor relative to other influence factors, constructing a fourth contrast matrix according to the re-determined second importance degree, replacing the content of the third contrast matrix with the content of the fourth contrast matrix, and returning to execute the step of calculating the consistency factor of the third contrast matrix according to the maximum characteristic value of the third contrast matrix and the order of the third contrast matrix;
and under the condition that the check coefficient of the third contrast matrix is smaller than a second preset threshold value, acquiring a second weight vector of the third contrast matrix according to the eigenvector of the third contrast matrix and the maximum eigenvalue of the third contrast matrix.
To achieve the above object, an embodiment of the present invention provides an apparatus for determining a target parking state, where the apparatus includes: a first obtaining module, a second obtaining module and a setting module, wherein,
the first obtaining module is configured to determine, according to a preset comparison scale table, a first importance degree of each influence factor relative to other influence factors except the current influence factor for each influence factor that affects the determination of the residence state, and obtain a first weight vector, where each influence factor includes: at least two of power consumption, signaling overhead, and latency consumption;
the second obtaining module is configured to, for each influence factor, determine, according to the preset comparison criteria table, a second importance degree of each preset resident state where the device resides with respect to other resident states except the current influence factor, and obtain a second weight vector, when the metric value of the current influence factor is lowest;
the setting module is configured to use a product of the first weight vector and the second weight vector as a third weight vector, and use a residence state corresponding to a weight factor in the third weight vector as a target residence state.
Optionally, the setting module is further configured to:
and determining the residence state corresponding to the maximum weight factor in the third weight vector as a target residence state.
Optionally, the first obtaining module is further configured to:
aiming at each influence factor influencing the determination of the residence state, acquiring a first importance degree of the influence factor relative to other influence factors except the current influence factor according to a preset comparison scale table, and constructing a first comparison matrix according to the first importance degree;
determining a first random consistency factor corresponding to the order of the first comparison matrix according to the order of the first comparison matrix and the corresponding relation between the order of the first comparison matrix and the first random consistency factor;
according to the maximum eigenvalue of the first comparison matrix and the order of the first comparison matrix, utilizing a formula
Figure GDA0002457268900000051
Calculating a consistency factor for the first comparison matrix, wherein,
CIAa consistency factor being a first comparison matrix; lambda [ alpha ]AIs the maximum eigenvalue of the first contrast matrix; n is the order of the first contrast matrix;
using a formula based on the consistency factor of the first contrast matrix and the first random consistency factor
Figure GDA0002457268900000052
Computing inspection coefficients for the first comparison matrix, wherein,
βAthe checking coefficient of the first comparison matrix; CIAA consistency factor being a first comparison matrix; RI (Ri)AIs a first random consistency factor;
under the condition that the checking coefficient of the first comparison matrix is not smaller than a first preset threshold value, re-determining the first importance degree of the influence factor relative to other influence factors, constructing a second comparison matrix according to the re-determined first importance degree, replacing the content of the first comparison matrix with the content of the second comparison matrix, and returning to execute the step of calculating the consistency factor of the first comparison matrix according to the maximum characteristic value of the first comparison matrix and the order of the first comparison matrix;
and under the condition that the checking coefficient of the first comparison matrix is smaller than a first preset threshold value, acquiring a first weight vector of the first comparison matrix according to the eigenvector of the first comparison matrix and the maximum eigenvalue of the first comparison matrix.
Optionally, the second obtaining module is further configured to:
for each influence factor, under the condition that the metric value of the current influence factor is lowest, determining a second importance degree of each resident state of the equipment resident relative to other resident states except the current resident state according to a preset comparison scale table, and constructing a third comparison matrix according to the second importance degree;
determining a second random consistency factor corresponding to the order of the third contrast matrix according to the order of the third contrast matrix and the corresponding relation between the order of the third contrast matrix and the second random consistency factor;
according to the maximum eigenvalue of the third contrast matrix and the order of the third contrast matrix, utilizing a formula
Figure GDA0002457268900000061
Calculating a consistency factor for the third contrast matrix, wherein,
CIBis the consistency factor of the third contrast matrix; lambda [ alpha ]BIs the maximum eigenvalue of the third contrast matrix; m is the order of the third contrast matrix;
according to the consistency factor of the third contrast matrix and the second random consistency factor, utilizing a formula
Figure GDA0002457268900000062
Computing inspection coefficients for the third contrast matrix, wherein,
βBthe checking coefficient of the third contrast matrix is; CIBIs the consistency factor of the third contrast matrix; RI (Ri)BIs a second random consistency factor;
under the condition that the checking coefficient of the third contrast matrix is not smaller than a second preset threshold value, re-determining a second importance degree of the influence factor relative to other influence factors, constructing a fourth contrast matrix according to the re-determined second importance degree, replacing the content of the third contrast matrix with the content of the fourth contrast matrix, and returning to execute the step of calculating the consistency factor of the third contrast matrix according to the maximum characteristic value of the third contrast matrix and the order of the third contrast matrix;
and under the condition that the check coefficient of the third contrast matrix is smaller than a second preset threshold value, acquiring a second weight vector of the third contrast matrix according to the eigenvector of the third contrast matrix and the maximum eigenvalue of the third contrast matrix.
In order to achieve the above object, an embodiment of the present invention further provides an electronic device, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the bus,
a memory for storing a computer program;
and a processor for implementing the steps of any of the above methods when executing the program stored in the memory.
To achieve the above object, an embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any of the above method embodiments.
The embodiment of the invention provides a method and a device for determining a target residence state and electronic equipment, wherein the method comprises the following steps: aiming at each influence factor influencing the determination of the residence state, determining a first importance degree of the influence factor relative to other influence factors according to a preset comparison scale table, and acquiring a first weight vector, wherein each influence factor at least comprises: power consumption, signaling overhead, latency consumption; aiming at each influence factor, under the condition that the metric value of the current influence factor is the lowest, determining the second importance degree of each preset resident state of the equipment resident relative to other resident states according to a preset comparison scale table, and acquiring a second weight vector; and determining a third weight vector according to the first weight vector and the second weight vector, and determining the residence state corresponding to the maximum weight factor in the third weight vector as a target residence state.
By applying the embodiment of the invention, the importance degree of each influence factor for the selection of the residence state is determined through the first weight vector, the importance degree of each preset residence state relative to each influence factor is determined through the second weight vector, and finally the weight factor of each preset residence state is determined, so that the preset residence state corresponding to the weight factor can be used as the target residence state. Of course, it is not necessary for any product or method of practicing the invention to achieve all of the above-described advantages at the same time.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of an application scenario of an embodiment of the present invention;
fig. 2 is a schematic diagram illustrating a method for determining a target parking status according to an embodiment of the present invention.
Fig. 3 is a flowchart illustrating a method for determining a target residence state according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an apparatus for determining a target residence state according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to solve the problem in the prior art, embodiments of the present invention provide a method and an apparatus for determining a target parking state, and an electronic device.
Fig. 1 is a schematic view of an application scenario of an embodiment of the present invention, and as shown in fig. 1, a base station 11 is used for providing a communication service, a first terminal device 12 and a second terminal device 13 are respectively in communication connection with the base station 11, and the first terminal device 12 undertakes an intelligent meter reading service and the second terminal device 13 undertakes a remote control service. Because the real-time performance requirement of the intelligent meter reading service on communication is not high, namely the time delay consumption is not very important, the main requirement is that the power consumption is low. Thus, the first terminal device 12 may reside in an IDLE state (IDLE state) or a SUSPENDED state (SUSPENDED state). The remote control service has high real-time requirement, i.e. it is very important to pay attention to delay consumption, and the second terminal device may reside in the DORMANT state or the eDRX state (enhanced discontinuous reception state).
Taking the example that the terminal device 12 applied in the embodiment of the present invention selects the target residing state, a process of selecting the target residing state by the terminal device 12 is introduced. Fig. 2 is a schematic diagram illustrating a principle of a method for determining a target parking state according to an embodiment of the present invention, and as shown in fig. 2, a decision target of the terminal device 12, that is, "select target parking state" may be used as a target layer; taking power consumption, signaling overhead and time delay consumption of three influencing factors influencing the determination of the resident state as a criterion layer; the four above-mentioned stay states are taken as the scheme layers. Measuring the importance degree of different influence factors on the terminal equipment 12 from three aspects of power consumption, signaling overhead and time delay consumption through the first weight vector; then measuring preference degrees of the terminal equipment 12 to each resident state from three aspects of power consumption, signaling overhead and time delay consumption by using a second weight vector; and finally, determining the target residence state of the terminal equipment 12 according to the first weight vector and the second weight vector.
Specifically, the process of obtaining the first weight vector may be: the terminal device 12 selects the importance degree of each influence factor relative to other influence factors according to the influence residence, constructs a first comparison matrix corresponding to the power consumption, signaling overhead and delay consumption according to the value of each importance degree, and then performs consistency check on the first comparison matrix. Because the time delay consumption comprises paging time delay and access time delay, a first intermediate matrix corresponding to the paging time delay and the access time delay is required to be constructed, and then consistency check is carried out on the first intermediate matrix; finally, a first weight vector is calculated according to the first comparison matrix of the consistency check and the first intermediate matrix of the consistency check.
Specifically, the process of obtaining the second weight vector may be: constructing a second intermediate matrix by taking the minimum power consumption as a target and taking the value of the second importance degree of each resident state relative to other resident states as an element, and then carrying out consistency check on the second intermediate matrix; constructing a third intermediate matrix with the aim of minimizing signaling overhead, constructing a fourth intermediate matrix with the aim of minimizing paging delay, and constructing a fifth intermediate matrix with the aim of minimizing access delay according to the method; and finally, performing consistency check on the second intermediate matrix, the third intermediate matrix and the fourth intermediate matrix, and generating a second weight vector according to the matrixes. The terminal device 12 calculates a third weight vector according to a product of the obtained first weight vector and the obtained second weight vector, and selects a target residence state corresponding to a weight factor according to a magnitude of the weight factor in the third weight vector, where the second comparison matrix includes: a second intermediate matrix, a third intermediate matrix, a fourth intermediate matrix, and a fifth intermediate matrix.
Fig. 3 is a flowchart illustrating a method for determining a target residence state according to an embodiment of the present invention, as shown in fig. 3, the method includes: :
s301: aiming at each influence factor influencing the determination of the residence state, determining a first importance degree of each influence factor relative to other influence factors except the current influence factor according to a preset comparison scale table, and acquiring a first weight vector, wherein each influence factor comprises: at least two of power consumption, signaling overhead, and latency consumption.
Specifically, for each influence factor that influences the determination of the residence state, a first importance degree of the influence factor relative to other influence factors except the current influence factor is obtained according to a preset comparison scale table, and a first comparison matrix is constructed according to the first importance degree; determining a first random consistency factor corresponding to the order of the first comparison matrix according to the order of the first comparison matrix and the corresponding relation between the order of the first comparison matrix and the first random consistency factor; according to the maximum eigenvalue of the first comparison matrix and the order of the first comparison matrix, utilizing a formula
Figure GDA0002457268900000101
Calculating a consistency factor for the first contrast matrix, wherein CIAA consistency factor being a first comparison matrix; lambda [ alpha ]AIs the maximum eigenvalue of the first contrast matrix; n is the order of the first contrast matrix; using a formula based on the consistency factor of the first contrast matrix and the first random consistency factor
Figure GDA0002457268900000102
Computing the test coefficients of the first comparison matrix, wherein βAThe checking coefficient of the first comparison matrix; CIAA consistency factor being a first comparison matrix; RI (Ri)AIs a first random consistency factor; under the condition that the checking coefficient of the first comparison matrix is not smaller than a first preset threshold value, re-determining the first importance degree of the influence factors relative to other influence factors, constructing a second comparison matrix according to the re-determined first importance degree, replacing the content of the first comparison matrix with the content of the second comparison matrix, and returning to execute the maximum eigenvalue of the first comparison matrix and the order of the first comparison matrixA step of calculating a consistency factor of the first contrast matrix; and under the condition that the checking coefficient of the first comparison matrix is smaller than a first preset threshold value, acquiring a first weight vector of the first comparison matrix according to the eigenvector of the first comparison matrix and the maximum eigenvalue of the first comparison matrix.
For example, the obtaining process of the first weight vector may be to construct a first comparison matrix corresponding to power consumption, signaling overhead, and delay consumption, and then perform consistency check on the first comparison matrix. Because the time delay consumption comprises paging time delay and access time delay, a first intermediate matrix corresponding to the paging time delay and the access time delay is required to be constructed, and then consistency check is carried out on the first intermediate matrix; finally, a first weight vector is calculated according to the first comparison matrix of the consistency check and the first intermediate matrix of the consistency check.
Illustratively, the first contrast matrix is constructed as follows:
table 1 is a preset comparison scale table, and according to table 1, a first importance degree of each influence factor with respect to other influence factors is determined, for example, if the first importance degree of power consumption with respect to signaling overhead is more important, the value of the first importance degree of power consumption with respect to signaling overhead is 5, and similarly, the value of the first importance degree of power consumption with respect to delay consumption can be determined; similarly, a value of a first degree of importance of signaling overhead relative to power consumption, overhead, and a value of a first degree of importance of delay consumption relative to power consumption, signaling overhead, may be determined.
TABLE 1
Comparison factor Quantized value
Of equal importance 1
Of slight importance 3
Of greater importance 5
Of strong importance 7
Of extreme importance 9
Intermediate values of two adjacent judgments 2，4，6，8
Constructing a first comparison matrix according to the determined values of the first importance degrees, wherein the construction method can be as follows: taking the value of the first importance degree of the power consumption relative to the signaling overhead and the value of the first importance degree relative to the time delay consumption as the first row elements of the first contrast matrix in turn; sequentially taking the value of the first importance degree of the signaling overhead relative to the power consumption, the value of the first importance degree relative to the signaling overhead and the value of the first importance degree relative to the time delay consumption as second row elements of the first contrast matrix; a first contrast matrix A is constructed by taking the value of the first importance degree of the time delay consumption relative to the power consumption, the value of the first importance degree relative to the signaling overhead and the value of the first importance degree relative to the time delay consumption as the second row element of the first contrast matrix in sequence1Comprises the following steps:
Figure GDA0002457268900000121
wherein the content of the first and second substances,
Figure GDA0002457268900000122
a value of a first degree of importance of power consumption relative to signaling overhead;
Figure GDA0002457268900000123
a value of a first degree of importance of power consumption relative to latency consumption;
Figure GDA0002457268900000124
is a value of a first degree of importance of the signalling overhead with respect to the delay consumption.
In the first contrast matrix, the elements of the first row may be values of a first degree of importance of the signaling overhead with respect to power consumption, delay consumption; the value of the first degree of importance of the delay consumption with respect to the signaling overhead and the power consumption may also be used, and the above arrangement order is not limited in the embodiment of the present invention. In practical applications, it is only necessary to ensure that the arrangement order of each row element in the first contrast matrix is consistent with the arrangement order of each column element.
Illustratively, the procedure for consistency checking of the first comparison matrix is as follows:
first, a first contrast matrix A is calculated1If the maximum eigenvalue of the first comparison matrix is calculated to be
Figure GDA0002457268900000125
Then the formula is utilized to obtain the final product,
Figure GDA0002457268900000126
calculating a first contrast matrix A1The consistency factor of (a), wherein,
Figure GDA0002457268900000127
a consistency factor being a first comparison matrix;
Figure GDA0002457268900000128
is the maximum eigenvalue of the first contrast matrix; n is the order of the first contrast matrixAnd (4) counting.
Table 2 is a table of correspondence between the matrix and the random consistency factor of the matrix, and then the random consistency factor of the first comparison matrix is determined according to table 2.
TABLE 2
Figure GDA0002457268900000129
The order of the first contrast matrix is 3, the random consistency factor of the first contrast matrix
Figure GDA00024572689000001210
Is 0.58.
Finally using the formula
Figure GDA00024572689000001211
The test coefficients of the first contrast matrix are calculated, wherein,
Figure GDA0002457268900000131
the checking coefficient of the first comparison matrix;
Figure GDA0002457268900000132
a consistency factor being a first comparison matrix;
Figure GDA0002457268900000133
is a random consistency factor of the first contrast matrix.
If the calculated checking coefficient of the first comparison matrix is 0.6 and is greater than the preset value of 0.1, the first comparison matrix does not meet the requirement of consistency, and the first comparison matrix needs to be reconstructed. The construction method comprises the steps of re-determining the first importance degree of each influence factor relative to other influence factors, constructing the second comparison matrix according to the method, replacing each element in the first comparison matrix with an element in the corresponding position in the second comparison matrix, and carrying out consistency check on the new first comparison matrix until the check coefficient of the first comparison matrix is less than or equal to 0.1.
In practical applications, the preset threshold is set by a user, including but not limited to 0.1.
Illustratively, the first intermediate matrix is constructed as follows:
according to table 1, a value of the first degree of importance of the paging delay with respect to the access delay is determined, and the value of the first degree of importance of the paging delay with respect to itself and the value of the first degree of importance with respect to the access delay may be used as a first row element of the first intermediate matrix; a first intermediate matrix A is constructed by taking the value of the first importance degree of the access delay relative to the paging delay and the value of the first importance degree relative to the access delay as the second row element of the first intermediate matrix2Comprises the following steps:
Figure GDA0002457268900000134
wherein the content of the first and second substances,
Figure GDA0002457268900000135
is the value of the first degree of importance of the paging delay relative to the access delay when the delay is minimal.
Illustratively, the procedure for consistency checking of the first intermediate matrix is as follows:
first, a first intermediate matrix A is calculated2If the maximum eigenvalue of the first intermediate matrix is calculated to be
Figure GDA0002457268900000136
Then the formula is utilized to obtain the final product,
Figure GDA0002457268900000137
computing a first intermediate matrix A2The consistency factor of (a), wherein,
Figure GDA0002457268900000138
a consistency factor of the first intermediate matrix;
Figure GDA0002457268900000139
is the maximum eigenvalue of the first intermediate matrix; n is the order of the first intermediate matrix.
The random consistency factor of the first intermediate matrix is then determined according to table 2. The order of the first intermediate matrix is 2, the random consistency factor of the first intermediate matrix
Figure GDA0002457268900000141
Is 0. However, in practical applications, the minimum value of the random consistency factor is usually 0.58, and thus the random consistency factor of the first intermediate matrix is 0.58.
Finally using the formula
Figure GDA0002457268900000142
The checking coefficients of the first intermediate matrix are calculated, wherein,
Figure GDA0002457268900000143
is the checking coefficient of the first intermediate matrix;
Figure GDA0002457268900000144
a consistency factor of the first intermediate matrix;
Figure GDA0002457268900000145
is a random consistency factor of the first intermediate matrix.
And if the calculated checking coefficient of the first intermediate matrix is 0.05 and is less than the preset value of 0.1, the first intermediate matrix meets the requirement of consistency and does not need to be reconstructed. If the calculated checking coefficient of the first intermediate matrix is greater than or equal to 0.1, the first intermediate matrix needs to be subjected to consistency check in a way of carrying out consistency check on the first comparison matrix.
Illustratively, the first weight vector is calculated from the first comparison matrix after the consistency check and the first intermediate matrix after the consistency check.
The first comparison matrix after the consistency check and the first intermediate matrix after the consistency check are respectively normalized, for example,
firstly, the characteristic vector of the first comparison matrix after consistency check is solved, each component in the characteristic vector is divided by the maximum characteristic value of the first comparison matrix after consistency check to obtain the normalized vector of the first comparison matrix after consistency check, if the normalized vector of the first comparison matrix after consistency check is the normalized vector of the first comparison matrix after consistency check
Figure GDA0002457268900000146
The normalized vector of the first intermediate matrix after passing the consistency check obtained by the method is
Figure GDA0002457268900000147
Then, a first weight vector is obtained according to the normalized vector of the first comparison matrix after consistency check and the normalized vector of the first intermediate matrix after consistency check, and the process specifically comprises the following steps:
due to the vector
Figure GDA0002457268900000148
Is/are as follows
Figure GDA0002457268900000149
Corresponding to the time delay consumption, therefore will
Figure GDA00024572689000001410
And vector
Figure GDA00024572689000001411
Respectively multiplying the elements to obtain a first weight vector as follows:
Figure GDA00024572689000001412
it should be emphasized that the device according to the embodiment of the present invention is Nb-Iot device (Narrow Band internet of Things device), and each of the above-mentioned camping statuses is 3GPP (3 GPP)rdGeneration PartnershipProject, third generation partnership project) organizes the various resident states specified in the frozen R13 report. The resident state is a management mechanism for managing a device, and aims to reduce the operation cost or power consumption.
S302: and aiming at each influence factor, under the condition that the metric value of the current influence factor is the lowest, determining the second importance degree of each preset resident state of the equipment resident relative to other resident states except the current influence factor according to the preset comparison metric table, and acquiring a second weight vector.
Specifically, for each influence factor, under the condition that the metric value of the current influence factor is the lowest, according to a preset comparison scale table, determining a second importance degree of each resident state of the device resident relative to other resident states except the current resident state, and constructing a third comparison matrix according to the second importance degree; determining a second random consistency factor corresponding to the order of the third contrast matrix according to the order of the third contrast matrix and the corresponding relation between the order of the third contrast matrix and the second random consistency factor; according to the maximum eigenvalue of the third contrast matrix and the order of the third contrast matrix, utilizing a formula
Figure GDA0002457268900000151
Calculating a consistency factor for the third contrast matrix, wherein CIBIs the consistency factor of the third contrast matrix; lambda [ alpha ]BIs the maximum eigenvalue of the third contrast matrix; m is the order of the third contrast matrix; according to the consistency factor of the third contrast matrix and the second random consistency factor, utilizing a formula
Figure GDA0002457268900000152
Computing inspection coefficients of the third contrast matrix, wherein βBThe checking coefficient of the third contrast matrix is; CIBIs the consistency factor of the third contrast matrix; RI (Ri)BIs a second random consistency factor; the check coefficient of the third contrast matrix is not less than the secondUnder the condition of presetting a threshold value, re-determining a second importance degree of the influence factor relative to other influence factors, constructing a fourth contrast matrix according to the re-determined second importance degree, replacing the content of the third contrast matrix with the content of the fourth contrast matrix, and returning to execute the step of calculating a consistency factor of the third contrast matrix according to the maximum eigenvalue of the third contrast matrix and the order of the third contrast matrix; and under the condition that the check coefficient of the third contrast matrix is smaller than a second preset threshold value, acquiring a second weight vector of the third contrast matrix according to the eigenvector of the third contrast matrix and the maximum eigenvalue of the third contrast matrix.
The obtaining process of the second weight vector may be that, first, a second intermediate matrix is constructed by taking the minimum power consumption as a target and taking the value of the second importance degree of each resident state relative to other resident states as an element, and then, consistency check is performed on the second intermediate matrix; constructing a third intermediate matrix by taking the target of minimizing signaling overhead and the value of the second importance degree of each resident state relative to other resident states as elements, constructing a fourth intermediate matrix by taking the value of the second importance degree of each resident state relative to other resident states as elements and constructing a fifth intermediate matrix by taking the value of the second importance degree of each resident state relative to other resident states as elements and the value of the minimum access delay as targets; and finally, calculating a second weight vector according to the second intermediate matrix after consistency check, the third intermediate matrix after consistency check, the fourth intermediate matrix after consistency check and the fifth intermediate matrix after consistency check, wherein the resident state comprises an IDLE state, a DORMANT state, a SUSPENDED state and an eDRX state, and the second comparison matrix comprises: a second intermediate matrix, a third intermediate matrix, a fourth intermediate matrix, and a fifth intermediate matrix.
For example, constructing the second intermediate matrix with the second importance value of each resident state relative to other resident states as an element with the goal of minimizing power consumption, and then performing consistency check on the second intermediate matrix may be:
according to table 1, the second importance level of each resident state relative to other resident states is determined with the goal of minimizing power consumption, for example, if the second importance level of the device resident IDLE state relative to the resident DORMANT state is between slightly important and more important with the goal of minimizing power consumption, the second importance level has a value of 4. Similarly, a value for a second degree of importance of the device park IDLE state relative to the park SUSPENDED state and the park eDRX state may be determined; similarly, values for a second degree of importance for the device camped DORMANT state relative to the camped SUSPENDED state and relative to the camped eDRX state may be determined; a value of a second degree of importance of the device camped state relative to the camped eDRX state is determined.
And constructing a second intermediate matrix according to the determined values of the second importance degrees, wherein the construction method can be as follows: the value of the second degree of importance of the device-resident IDLE state with respect to itself, the value of the second degree of importance with respect to the resident DORMANT state, the value of the second degree of importance with respect to the resident SUSPENDED state, and the value of the second degree of importance with respect to the resident eDRX state are sequentially set as first row elements of a second intermediate matrix; taking the value of the second degree of importance of the device camping DORMANT state relative to the camping IDLE state, the value of the second degree of importance relative to the device camping IDLE state, the value of the second degree of importance relative to the camping state, and the value of the second degree of importance relative to the camping eDRX state as second row elements of a second intermediate matrix in sequence; taking the value of the second degree of importance of the device-camped SUSPENDED state with respect to the camped IDLE state, the value of the second degree of importance with respect to the camped DORMANT state, the value of the second degree of importance with respect to itself, and the value of the second degree of importance with respect to the camped eDRX state as the third row elements of the second intermediate matrix in this order; taking the value of the second degree of importance of the device-resident eDRX state relative to the resident IDLE state, the value of the second degree of importance relative to the resident DORMANT state, the value of the second degree of importance relative to the resident SUSPENDED state, and the value of the second degree of importance relative to itself as a second intermediate matrix in sequenceThe fourth row of elements. Second intermediate matrix B of the construction1Comprises the following steps:
Figure GDA0002457268900000171
wherein the content of the first and second substances,
Figure GDA0002457268900000172
a value of a second degree of importance of the device residing in the IDLE state relative to residing in the DORMANT state to target minimizing power consumption;
Figure GDA0002457268900000173
a second importance value of device residing in an IDLE state and residing in a SUSPENDED state to target minimizing power consumption;
Figure GDA0002457268900000174
a value of a second degree of importance for the device to reside in the IDLE state relative to the eDRX state with a goal of minimizing power consumption;
Figure GDA0002457268900000175
a value of a second degree of importance of the device residing in the DORMANT state relative to residing in the SUSPENDED state to target minimizing power consumption;
Figure GDA0002457268900000176
a value of a second degree of importance for the device to camp in the DORMANT state relative to camping in the eDRX state to target minimizing power consumption;
Figure GDA0002457268900000177
to target a minimum power consumption, the device resides in the SUSPENDED state at a second level of importance relative to the DRX state.
Illustratively, the process of consistency checking of the second intermediate matrix is as follows:
first, a second intermediate matrix B is calculated1If the maximum eigenvalue of the second intermediate matrix is calculatedMaximum eigenvalue is
Figure GDA0002457268900000178
Then the formula is utilized to obtain the final product,
Figure GDA0002457268900000181
computing a second intermediate matrix B1The consistency factor of (a), wherein,
Figure GDA0002457268900000182
is the consistency factor of the second intermediate matrix;
Figure GDA0002457268900000183
is the maximum eigenvalue of the second intermediate matrix; n is the order of the second intermediate matrix.
The random consistency factor of the second intermediate matrix is then determined according to table 2.
The order of the second intermediate matrix is 4, the random consistency factor of the second intermediate matrix
Figure GDA0002457268900000184
Is 0.9.
Finally using the formula
Figure GDA0002457268900000185
The test coefficients of the first contrast matrix are calculated, wherein,
Figure GDA0002457268900000186
the checking coefficient of the second intermediate matrix;
Figure GDA0002457268900000187
is the consistency factor of the second intermediate matrix;
Figure GDA0002457268900000188
is a random consistency factor of the second intermediate matrix.
And if the calculated checking coefficient of the second intermediate matrix is 0.6 and is greater than the preset value of 0.1, the second intermediate matrix does not meet the requirement of consistency and needs to be reconstructed. The construction mode is to determine the first importance degree of each influence factor relative to other influence factors again, then construct a new second intermediate matrix according to the above mode, then replace each element in the second intermediate matrix with the element at the corresponding position in the new second intermediate matrix, and then carry out consistency check on the second intermediate matrix after replacing elements until the check coefficient of the second intermediate matrix is less than or equal to 0.1.
In practical applications, the preset threshold is set by a user, including but not limited to 0.1.
Similarly, a third intermediate matrix, a fourth intermediate matrix and a fifth intermediate matrix are constructed according to the construction process of the second intermediate matrix; and carrying out consistency check on the third intermediate matrix, the fourth intermediate matrix and the fifth intermediate matrix according to the consistency check on the second intermediate matrix.
For example, constructing the third intermediate matrix with the goal of minimizing signaling overhead may be:
Figure GDA0002457268900000189
wherein the content of the first and second substances,
Figure GDA00024572689000001810
a value of a second degree of importance for a device to reside in an IDLE state relative to a DORMANT state, to target minimal signaling overhead;
Figure GDA0002457268900000191
a value of a second degree of importance of the device residing in the IDLE state relative to residing in the SUSPENDED state, with a goal of minimizing signaling overhead;
Figure GDA0002457268900000192
a value of a second degree of importance for a device to camp in an IDLE state relative to camping in an eDRX state with a goal of minimizing signaling overhead;
Figure GDA0002457268900000193
a value of a second degree of importance of the device residing in the DORMANT state relative to residing in the SUSPENDED state to target minimizing signaling overhead;
Figure GDA0002457268900000194
a value of a second degree of importance for a device to camp in a DORMANT state relative to camping in an eDRX state, with a goal of minimizing signaling overhead;
Figure GDA0002457268900000195
to target a minimum signaling overhead, the device resides in a SUSPENDED state relative to a value of a second importance that resides in eDRX state.
According to the method for checking the consistency of the second intermediate matrix, the third intermediate matrix B is constructed2And (5) carrying out consistency check.
For example, the fourth intermediate matrix is constructed with the minimum paging delay as a target:
Figure GDA0002457268900000196
wherein the content of the first and second substances,
Figure GDA0002457268900000197
a value of a second degree of importance for a device residing in an IDLE state relative to a device residing in a DORMANT state, to target minimizing paging latency overhead;
Figure GDA0002457268900000198
a value of a second degree of importance for the device to reside in the IDLE state relative to the suspend state with a goal of minimizing paging latency consumption;
Figure GDA0002457268900000199
a value of a second degree of importance for a device to camp in an IDLE state relative to camping in an eDRX state with a goal of minimizing paging latency consumption;
Figure GDA00024572689000001910
a value of a second degree of importance for the device to reside in the DORMANT state relative to the suspend state to target minimizing paging latency consumption;
Figure GDA00024572689000001911
a value of a second degree of importance for a device to camp in a DORMANT state relative to camping in an eDRX state with a goal of minimizing paging latency consumption;
Figure GDA00024572689000001912
to target minimum paging latency consumption, the device resides in a SUSPENDED state relative to a value of a second degree of importance residing in eDRX state.
A fourth intermediate matrix B is formed by a method for checking the consistency of the second intermediate matrix3And (5) carrying out consistency check.
For example, the fifth intermediate matrix constructed with the goal of minimizing access latency may be:
Figure GDA0002457268900000201
wherein the content of the first and second substances,
Figure GDA0002457268900000202
a value of a second degree of importance for a device residing in an IDLE state relative to a device residing in a DORMANT state, with a goal of minimizing access latency;
Figure GDA0002457268900000203
a value of a second importance of the device residing in the IDLE state relative to residing in the SUSPENDED state to target minimizing access latency;
Figure GDA0002457268900000204
a value of a second importance of the device residing in the IDLE state relative to the eDRX state with a goal of minimizing access latency;
Figure GDA0002457268900000205
a value of a second degree of importance for the device to reside in the DORMANT state relative to the SUSPENDED state, with a goal of minimizing access latency;
Figure GDA0002457268900000206
a value of a second importance of the device residing in the DORMANT state relative to the eDRX state for minimizing access latency;
Figure GDA0002457268900000207
to target minimization of access latency, the device resides in a SUSPENDED state relative to a value of a second importance that resides in eDRX state.
According to the method for checking the consistency of the second intermediate matrix, the fifth intermediate matrix B is constructed4And (5) carrying out consistency check.
Illustratively, the second intermediate matrix after passing the consistency check, the third intermediate matrix after passing the consistency check, the fourth intermediate matrix after passing the consistency check, and the fifth intermediate matrix after passing the consistency check.
The second intermediate matrix after passing the consistency check, the third intermediate matrix after passing the consistency check, the fourth intermediate matrix after passing the consistency check, and the fifth intermediate matrix after passing the consistency check are respectively subjected to normalization processing, for example,
firstly, the eigenvector of the second intermediate matrix after consistency check is solved, each component in the eigenvector is divided by the maximum eigenvalue of the second intermediate matrix after consistency check to obtain the normalized vector of the second intermediate matrix after consistency check, if the normalized vector of the second intermediate matrix after consistency check is the normalized vector of the second intermediate matrix after consistency check
Figure GDA0002457268900000208
The normalized vector of the third intermediate matrix after consistency check obtained by the method is
Figure GDA0002457268900000209
The normalized vector of the fourth intermediate matrix after consistency check obtained by the method is
Figure GDA00024572689000002010
The normalized vector of the fifth intermediate matrix after consistency check obtained by the method is
Figure GDA0002457268900000211
Then, according to the normalized vector, a second weight vector is obtained
Figure GDA0002457268900000212
S303: and taking the product of the first weight vector and the second weight vector as a third weight vector, and taking the residence state corresponding to the weight factor in the third weight vector as a target residence state.
For example, the formula w ═ w may be usedB·wAAnd calculating a third weight vector:
w＝wB·wA＝(widlewsuspwdormwedrx)
wherein, widleA weighting factor for a device that tends to reside in an IDLE state; w is asuspA weighting factor that is a tendency of a device to reside in a SUSPENDED state; w is adormA weighting factor for devices that tend to reside in a DORMANT state; w is aedrxA weight factor for devices that tend to camp in eDRX state.
By applying the embodiment of fig. 3 of the present invention, the importance degree of each influence factor for the selection of the residence state is determined by the first weight vector, the importance degree of each preset residence state relative to each influence factor is determined by the second weight vector, and finally, the weight factor of each preset residence state is determined, so that the preset residence state corresponding to the weight factor can be used as the target residence state.
Specifically, the parking state corresponding to the largest weight factor in the third weight vector may be determined as the target parking state.
If w in the weight vector widleIf the factor is maximum, setting the IDLE state as a target resident state; if w in the weight vector wsuspSetting the SUSPENDED state to the target park state if the factor is maximum; if w in the weight vector wdormIf the factor is maximum, setting the DORMANT to be in a target residence state; if w in the weight vector wedrxThe factor is maximum, then the eDRX state is set to the target camping state.
It will be appreciated that in actual practice, different devices or devices tasked with different tasks will have different state-resident preferences. The device takes a certain parking state as a target parking state, which indicates that the state parking preference of the device is the target parking state, i.e. the device is biased to park in the target parking state.
By applying the above embodiment of the present invention, the residence state corresponding to the largest weight factor in the third weight vector is determined as the target residence state, so that the optimal target residence state can be selected for the device.
Corresponding to the embodiment shown in fig. 3 of the present invention, an apparatus for determining a target parking state is further provided in the embodiment of the present invention.
Fig. 4 is a schematic structural diagram of an apparatus for determining a target residence state according to an embodiment of the present invention, as shown in fig. 4, the apparatus includes: a first acquisition module 401, a second acquisition module 402, and a setup module 403, wherein,
the first obtaining module 401 is configured to, for each influence factor that affects the determination of the residence state, determine, according to a preset comparison scale table, a first importance degree of each influence factor relative to other influence factors except the current influence factor, and obtain a first weight vector, where each influence factor includes: at least two of power consumption, signaling overhead, and latency consumption.
The second obtaining module 402 is configured to, for each influence factor, determine, according to the preset comparison criteria table, a second importance degree of each preset residing state of the device with respect to other residing states except for the current influence factor, and obtain a second weight vector, when the metric value of the current influence factor is the lowest.
The setting module 403 is configured to use a product of the first weight vector and the second weight vector as a third weight vector, and use a residence state corresponding to a weight factor in the third weight vector as a target residence state.
Optionally, in a specific implementation manner of the embodiment of the present invention, the setting module 403 is further configured to:
and determining the residence state corresponding to the maximum weight factor in the third weight vector as a target residence state.
Optionally, in a specific implementation manner of the embodiment of the present invention, the first obtaining module 401 is further configured to:
aiming at each influence factor influencing the determination of the residence state, acquiring a first importance degree of the influence factor relative to other influence factors except the current influence factor according to a preset comparison scale table, and constructing a first comparison matrix according to the first importance degree;
determining a first random consistency factor corresponding to the order of the first comparison matrix according to the order of the first comparison matrix and the corresponding relation between the order of the first comparison matrix and the first random consistency factor;
according to the maximum eigenvalue of the first comparison matrix and the order of the first comparison matrix, utilizing a formula
Figure GDA0002457268900000231
Calculating a consistency factor for the first comparison matrix, wherein,
CIAa consistency factor being a first comparison matrix; lambda [ alpha ]AIs the maximum eigenvalue of the first contrast matrix; n is the order of the first contrast matrix;
according to one of the first contrast matrixA consistency factor, and said first random consistency factor, using a formula
Figure GDA0002457268900000232
Computing inspection coefficients for the first comparison matrix, wherein,
βAthe checking coefficient of the first comparison matrix; CIAA consistency factor being a first comparison matrix; RI (Ri)AIs a first random consistency factor;
under the condition that the checking coefficient of the first comparison matrix is not smaller than a first preset threshold value, re-determining the first importance degree of the influence factor relative to other influence factors, constructing a second comparison matrix according to the re-determined first importance degree, replacing the content of the first comparison matrix with the content of the second comparison matrix, and returning to execute the step of calculating the consistency factor of the first comparison matrix according to the maximum characteristic value of the first comparison matrix and the order of the first comparison matrix;
and under the condition that the checking coefficient of the first comparison matrix is smaller than a first preset threshold value, acquiring a first weight vector of the first comparison matrix according to the eigenvector of the first comparison matrix and the maximum eigenvalue of the first comparison matrix.
Optionally, in a specific implementation manner of the embodiment of the present invention, the second obtaining module 202 is further configured to:
for each influence factor, under the condition that the metric value of the current influence factor is lowest, determining a second importance degree of each resident state of the equipment resident relative to other resident states except the current resident state according to a preset comparison scale table, and constructing a third comparison matrix according to the second importance degree;
determining a second random consistency factor corresponding to the order of the third contrast matrix according to the order of the third contrast matrix and the corresponding relation between the order of the third contrast matrix and the second random consistency factor;
maximum eigenvalue according to the third contrast matrixThe order of the third contrast matrix is calculated by using a formula
Figure GDA0002457268900000241
Calculating a consistency factor for the third contrast matrix, wherein,
CIBis the consistency factor of the third contrast matrix; lambda [ alpha ]BIs the maximum eigenvalue of the third contrast matrix; m is the order of the third contrast matrix;
according to the consistency factor of the third contrast matrix and the second random consistency factor, utilizing a formula
Figure GDA0002457268900000242
Computing inspection coefficients for the third contrast matrix, wherein,
βBthe checking coefficient of the third contrast matrix is; CIBIs the consistency factor of the third contrast matrix; RI (Ri)BIs a second random consistency factor;
under the condition that the checking coefficient of the third contrast matrix is not smaller than a second preset threshold value, re-determining a second importance degree of the influence factor relative to other influence factors, constructing a fourth contrast matrix according to the re-determined second importance degree, replacing the content of the third contrast matrix with the content of the fourth contrast matrix, and returning to execute the step of calculating the consistency factor of the third contrast matrix according to the maximum characteristic value of the third contrast matrix and the order of the third contrast matrix;
and under the condition that the check coefficient of the third contrast matrix is smaller than a second preset threshold value, acquiring a second weight vector of the third contrast matrix according to the eigenvector of the third contrast matrix and the maximum eigenvalue of the third contrast matrix.
By applying the embodiment shown in fig. 4 of the present invention, the importance degree of each influence factor for the selection of the residence state is determined by the first weight vector, the importance degree of each preset residence state relative to each influence factor is determined by the second weight vector, and finally, the weight factor of each preset residence state is determined, so that the preset residence state corresponding to the weight factor can be taken as the target residence state.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, as shown in fig. 5, the electronic device includes a processor 501, a communication interface 502, a memory 503, and a communication bus 505, where the processor 501, the communication interface 502, and the memory 503 complete communication with each other through the bus,
a memory 503 for storing a computer program;
the processor 501 is configured to implement the following steps when executing the program stored in the memory:
aiming at each influence factor influencing the determination of the residence state, determining a first importance degree of each influence factor relative to other influence factors except the current influence factor according to a preset comparison scale table, and acquiring a first weight vector, wherein each influence factor comprises: at least two of power consumption, signaling overhead, and latency consumption;
for each influence factor, under the condition that the metric value of the current influence factor is lowest, determining a second importance degree of each preset resident state of the equipment resident relative to other resident states except the current influence factor according to the preset comparison metric table, and acquiring a second weight vector;
and taking the product of the first weight vector and the second weight vector as a third weight vector, and taking the residence state corresponding to the weight factor in the third weight vector as a target residence state.
By applying the embodiment shown in fig. 5 of the present invention, the importance degree of each influence factor for the selection of the residence state is determined by the first weight vector, the importance degree of each preset residence state relative to each influence factor is determined by the second weight vector, and finally, the weight factor of each preset residence state is determined, so that the preset residence state corresponding to the weight factor can be used as the target residence state.
An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any of the above method embodiments.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, 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, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related 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 apparatus, the electronic device, the computer-readable medium, and the computer program embodiment, since they are substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims (6)

1. A method for determining a target parking status, the method comprising:
aiming at each influence factor influencing the determination of the residence state, determining a first importance degree of each influence factor relative to other influence factors except the current influence factor according to a preset comparison scale table, and acquiring a first weight vector, wherein each influence factor comprises: at least two of power consumption, signaling overhead, and latency consumption;
aiming at each influence factor, under the condition that the metric value of the current influence factor is the lowest, determining the second importance degree of each preset resident state of the equipment resident relative to other resident states except the current influence factor according to the preset comparison metric table, and acquiring a second weight vector;
taking the product of the first weight vector and the second weight vector as a third weight vector, and taking the residence state corresponding to the weight factor in the third weight vector as a target residence state;
the determining, according to a preset comparison scale table, a first importance degree of each influence factor relative to other influence factors except the current influence factor for each influence factor determined by the influence residence state, and obtaining a first weight vector includes:
aiming at each influence factor influencing the determination of the residence state, acquiring a first importance degree of the influence factor relative to other influence factors except the current influence factor according to a preset comparison scale table, and constructing a first comparison matrix according to the first importance degree;
determining a first random consistency factor corresponding to the order of the first comparison matrix according to the order of the first comparison matrix and the corresponding relation between the order of the first comparison matrix and the first random consistency factor;
according to the maximum eigenvalue of the first comparison matrix and the order of the first comparison matrix, utilizing a formula
Figure FDA0002577008860000011
Calculating a consistency factor of the first comparison matrix;
wherein, CIAA consistency factor being a first comparison matrix; lambda [ alpha ]AIs the maximum eigenvalue of the first contrast matrix; n is the order of the first contrast matrix;
according to the firstComparing the consistency factor of the matrix with the first random consistency factor by using a formula
Figure FDA0002577008860000021
Calculating the checking coefficient of the first comparison matrix;
wherein, βAThe checking coefficient of the first comparison matrix; CIAA consistency factor being a first comparison matrix; RI (Ri)AIs a first random consistency factor;
under the condition that the checking coefficient of the first comparison matrix is not smaller than a first preset threshold value, re-determining the first importance degree of the influence factor relative to other influence factors, constructing a second comparison matrix according to the re-determined first importance degree, replacing the content of the first comparison matrix with the content of the second comparison matrix, and returning to execute the step of calculating the consistency factor of the first comparison matrix according to the maximum characteristic value of the first comparison matrix and the order of the first comparison matrix;
under the condition that the check coefficient of the first comparison matrix is smaller than a first preset threshold value, acquiring a first weight vector of the first comparison matrix according to the eigenvector of the first comparison matrix and the maximum eigenvalue of the first comparison matrix;
the determining, for each influence factor, a second importance degree of each preset resident state of the device resident with respect to other resident states except the current influence factor according to a preset comparison scale table under the condition that the metric value of the current influence factor is lowest, and acquiring a second weight vector, includes:
for each influence factor, under the condition that the metric value of the current influence factor is lowest, determining a second importance degree of each resident state of the equipment resident relative to other resident states except the current resident state according to a preset comparison scale table, and constructing a third comparison matrix according to the second importance degree;
determining a second random consistency factor corresponding to the order of the third contrast matrix according to the order of the third contrast matrix and the corresponding relation between the order of the third contrast matrix and the second random consistency factor;
according to the maximum eigenvalue of the third contrast matrix and the order of the third contrast matrix, utilizing a formula
Figure FDA0002577008860000022
Calculating a consistency factor of the third contrast matrix;
wherein, CIBIs the consistency factor of the third contrast matrix; lambda [ alpha ]BIs the maximum eigenvalue of the third contrast matrix; m is the order of the third contrast matrix;
according to the consistency factor of the third contrast matrix and the second random consistency factor, utilizing a formula
Figure FDA0002577008860000031
Calculating the checking coefficient of the third contrast matrix;
wherein, βBThe checking coefficient of the third contrast matrix is; CIBIs the consistency factor of the third contrast matrix; RI (Ri)BIs a second random consistency factor;
under the condition that the checking coefficient of the third contrast matrix is not smaller than a second preset threshold value, re-determining a second importance degree of the influence factor relative to other influence factors, constructing a fourth contrast matrix according to the re-determined second importance degree, replacing the content of the third contrast matrix with the content of the fourth contrast matrix, and returning to execute the step of calculating the consistency factor of the third contrast matrix according to the maximum characteristic value of the third contrast matrix and the order of the third contrast matrix;
and under the condition that the check coefficient of the third contrast matrix is smaller than a second preset threshold value, acquiring a second weight vector of the third contrast matrix according to the eigenvector of the third contrast matrix and the maximum eigenvalue of the third contrast matrix.
2. The method according to claim 1, wherein the taking the parking state corresponding to the weight factor in the third weight vector as the target parking state comprises:
and determining the residence state corresponding to the maximum weight factor in the third weight vector as a target residence state.
3. An apparatus for determining a target parking state, the apparatus comprising: a first obtaining module, a second obtaining module and a setting module, wherein,
the first obtaining module is configured to determine, according to a preset comparison scale table, a first importance degree of each influence factor relative to other influence factors except the current influence factor for each influence factor that affects the determination of the residence state, and obtain a first weight vector, where each influence factor includes: at least two of power consumption, signaling overhead, and latency consumption;
the second obtaining module is configured to, for each influence factor, determine, according to the preset comparison scale table, a second importance degree of each preset resident state where the device resides with respect to other resident states except the current influence factor, and obtain a second weight vector, when the metric value of the current influence factor is lowest;
the setting module is configured to use a product of the first weight vector and the second weight vector as a third weight vector, and use a residence state corresponding to a weight factor in the third weight vector as a target residence state;
the first obtaining module is further configured to:
aiming at each influence factor influencing the determination of the residence state, acquiring a first importance degree of the influence factor relative to other influence factors except the current influence factor according to a preset comparison scale table, and constructing a first comparison matrix according to the first importance degree;
determining a first random consistency factor corresponding to the order of the first comparison matrix according to the order of the first comparison matrix and the corresponding relation between the order of the first comparison matrix and the first random consistency factor;
according to the maximum eigenvalue of the first comparison matrix and the order of the first comparison matrix, utilizing a formula
Figure FDA0002577008860000041
Calculating a consistency factor of the first comparison matrix;
wherein, CIAA consistency factor being a first comparison matrix; lambda [ alpha ]AIs the maximum eigenvalue of the first contrast matrix; n is the order of the first contrast matrix;
using a formula based on the consistency factor of the first contrast matrix and the first random consistency factor
Figure FDA0002577008860000042
Calculating the checking coefficient of the first comparison matrix;
wherein, βAThe checking coefficient of the first comparison matrix; CIAA consistency factor being a first comparison matrix; RI (Ri)AIs a first random consistency factor;
under the condition that the checking coefficient of the first comparison matrix is not smaller than a first preset threshold value, re-determining the first importance degree of the influence factor relative to other influence factors, constructing a second comparison matrix according to the re-determined first importance degree, replacing the content of the first comparison matrix with the content of the second comparison matrix, and returning to execute the step of calculating the consistency factor of the first comparison matrix according to the maximum characteristic value of the first comparison matrix and the order of the first comparison matrix;
under the condition that the check coefficient of the first comparison matrix is smaller than a first preset threshold value, acquiring a first weight vector of the first comparison matrix according to the eigenvector of the first comparison matrix and the maximum eigenvalue of the first comparison matrix;
the second obtaining module is further configured to:
for each influence factor, under the condition that the metric value of the current influence factor is lowest, determining a second importance degree of each resident state of the equipment resident relative to other resident states except the current resident state according to a preset comparison scale table, and constructing a third comparison matrix according to the second importance degree;
determining a second random consistency factor corresponding to the order of the third contrast matrix according to the order of the third contrast matrix and the corresponding relation between the order of the third contrast matrix and the second random consistency factor;
according to the maximum eigenvalue of the third contrast matrix and the order of the third contrast matrix, utilizing a formula
Figure FDA0002577008860000051
Calculating a consistency factor of the third contrast matrix;
wherein, CIBIs the consistency factor of the third contrast matrix; lambda [ alpha ]BIs the maximum eigenvalue of the third contrast matrix; m is the order of the third contrast matrix;
according to the consistency factor of the third contrast matrix and the second random consistency factor, utilizing a formula
Figure FDA0002577008860000052
Calculating the checking coefficient of the third contrast matrix;
wherein, βBThe checking coefficient of the third contrast matrix is; CIBIs the consistency factor of the third contrast matrix; RI (Ri)BIs a second random consistency factor;
under the condition that the checking coefficient of the third contrast matrix is not smaller than a second preset threshold value, re-determining a second importance degree of the influence factor relative to other influence factors, constructing a fourth contrast matrix according to the re-determined second importance degree, replacing the content of the third contrast matrix with the content of the fourth contrast matrix, and returning to execute the step of calculating the consistency factor of the third contrast matrix according to the maximum characteristic value of the third contrast matrix and the order of the third contrast matrix;
and under the condition that the check coefficient of the third contrast matrix is smaller than a second preset threshold value, acquiring a second weight vector of the third contrast matrix according to the eigenvector of the third contrast matrix and the maximum eigenvalue of the third contrast matrix.
4. The apparatus of claim 3, wherein the setup module is further configured to:
and determining the residence state corresponding to the maximum weight factor in the third weight vector as a target residence state.
5. An electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the bus,
a memory for storing a computer program;
a processor for implementing the method steps of any of claims 1-2 when executing a program stored in the memory.
6. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of the claims 1-2.
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