Sensor heat output threshold optimization method based on wireless sensor network, storage medium, equipment and computer program product

Abstract

The invention discloses a sensor heat output threshold optimization method based on a wireless sensor network, a storage medium, equipment and a computer program product, and the method comprises the steps: finding out all historical log data exceeding a sensor heat output threshold according to the sensor heat output threshold set in the wireless sensor network; the transmission energy consumption of the wireless sensor network is predicted through the found historical log data, and if the predicted transmission energy consumption of the wireless sensor network exceeds a specified energy consumption threshold value, the sensor heat output threshold value does not need to be updated; otherwise, optimizing the heat output threshold value of the sensor through a genetic algorithm. Aiming at the condition that the heat output threshold value of the sensor is unreasonable in design, the heat output threshold value of the sensor is optimized through the genetic algorithm, so that the normal operation of the wireless sensor network is ensured to the greatest extent.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent sensors, and in particular, relates to a sensor thermal output threshold optimization method based on a wireless sensor network, a storage medium, a device and a computer program product. Background Art

[0002] Wireless sensor networks collect various environmental data through sensors and transmit the collected data to the cloud or local server in real time through wireless networks. The heat generated by the sensor during operation is the sensor thermal output. As the temperature of the sensor increases, the sensor thermal output will increase, which may affect the quality of data transmission and reception, resulting in signal attenuation during data transmission. The sensor thermal output may generate additional electromagnetic noise, which will interfere with the transmission signal and reduce the signal-to-noise ratio and transmission quality of the signal transmission. The increase in sensor thermal output may also lead to increased energy consumption and affect the energy efficiency of the wireless sensor network. Therefore, in order to minimize the adverse effects of sensor thermal output, the thermal output threshold of each sensor is usually set when the wireless sensor network is deployed, thereby reducing the impact of sensor thermal output on the wireless sensor network during data transmission.

[0003] When the sensor thermal output exceeds the sensor thermal output threshold, the sensor will stop working, causing the wireless sensor network to suspend data transmission. However, the current sensor thermal output threshold is determined based on experience without considering the actual working environment of the sensor, resulting in the setting of the sensor thermal output threshold being inconsistent with the actual situation, affecting the performance of the wireless sensor network. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention provides a sensor thermal output threshold optimization method, storage medium, device and computer program product based on a wireless sensor network. In view of the unreasonable design of the sensor thermal output threshold, the sensor thermal output threshold is optimized through a genetic algorithm, thereby maximally ensuring the normal operation of the wireless sensor network.

[0005] To achieve the above technical objectives, the present invention adopts the following technical solution: a method for optimizing the thermal output threshold of a sensor based on a wireless sensor network, specifically comprising the following steps:

[0006] Step S1, according to the sensor thermal output threshold value set in the wireless sensor network, find out all historical log data that exceeds the sensor thermal output threshold value;

[0007] Step S2: predicting the transmission energy consumption of the wireless sensor network by finding the historical log data; if the predicted transmission energy consumption of the wireless sensor network exceeds the specified energy consumption threshold, there is no need to update the sensor thermal output threshold; otherwise, optimizing the sensor thermal output threshold by a genetic algorithm.

[0008] Furthermore, the specific process of the transmission energy consumption prediction of the wireless sensor network in step S2 is as follows:

[0009] E=E s ×L+E r ×N n ×L

[0010] Where E represents the predicted value of transmission energy consumption of wireless sensor networks, E s represents the energy consumed by the sensor to send data, L represents the length of the broadcast data, and E r N represents the energy consumed by the wireless sensor network to receive data. n Indicates the number of neighbor nodes.

[0011] Furthermore, the specific process of optimizing the sensor thermal output threshold value by genetic algorithm in step S2 is:

[0012] Step S2.1, using the predicted value of the transmission energy consumption of the wireless sensor network as the fitness function of the genetic algorithm, randomly initializing a number of sensor thermal output thresholds as the initial population of the genetic algorithm, using the sensor thermal output thresholds in the population as individuals of the genetic algorithm, and setting the maximum number of iterations of the genetic algorithm;

[0013] Step S2.2, use all individuals to run the wireless sensor network, find all the log data that exceeds the individuals, and calculate the fitness function;

[0014] Step S2.3, retain the individual with the lowest fitness function value, perform selection, crossover and mutation, and obtain updated individuals;

[0015] Step S2.4, repeat steps S2.2-2.3 until the maximum number of iterations is reached, and take the individual with the lowest fitness function value as the optimal sensor thermal output threshold.

[0016] Furthermore, the specific process of crossover in step S2.3 is as follows:

[0017]

[0018] Among them, Pc n represents the crossover rate of individuals in the nth iteration, k1 represents the constant factor controlling the crossover rate of individuals, F n Indicates the fitness function value of the retained individual in the nth iteration, max(F n) represents the maximum value of the individual fitness function in the population under the nth iteration, min(F n ) represents the minimum value of the individual fitness function in the population at the nth iteration.

[0019] Furthermore, the specific process of mutation in step S2.3 is:

[0020]

[0021] Among them, Pm n represents the individual mutation rate under the nth iteration, k2 represents the constant factor controlling the individual mutation rate, represents the mean value of the fitness function of all individuals in the population in the first n iterations, F min represents the minimum fitness function value of all individuals in the population in the first n iterations, min(F n ) represents the minimum value of the individual fitness function in the population at the nth iteration.

[0022] Furthermore, the present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the sensor thermal output threshold optimization method based on a wireless sensor network.

[0023] Furthermore, the present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for optimizing the thermal output threshold of a sensor based on a wireless sensor network is implemented.

[0024] Furthermore, the present invention also provides a computer program product, including a computer program, and when the computer program is executed by a processor, the method for optimizing the thermal output threshold of a sensor based on a wireless sensor network is implemented.

[0025] Compared with the prior art, the present invention has the following beneficial effects: the transmission energy consumption of the wireless sensor network can more accurately and reasonably reflect the heat output situation in the entire network. The sensor heat output threshold optimization method based on the wireless sensor network of the present invention finds out the unreasonable sensor heat output threshold according to the predicted value of the transmission energy consumption of the wireless sensor network, thereby optimizing the unreasonable sensor heat output threshold, making the wireless sensor network suitable for more application scenarios; and, the present invention optimizes the unreasonable sensor heat output threshold through a genetic algorithm, and finds out the optimal sensor heat output threshold through the global search capability of the genetic algorithm. The result is more accurate, which can meet the long-term working requirements of the wireless sensor network, reduce downtime, and improve the working stability of the wireless sensor network. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of the sensor thermal output threshold optimization method based on wireless sensor network of the present invention;

[0027] Figure 2 Flowchart for optimizing sensor thermal output threshold using genetic algorithm. DETAILED DESCRIPTION

[0028] The technical solution of the present invention is further explained below in conjunction with the accompanying drawings.

[0029] like Figure 1 The flowchart of the sensor thermal output threshold optimization method based on the wireless sensor network of the present invention is as follows:

[0030] Step S1, for the wireless sensor network, during the operation of the wireless sensor network, if the sensor thermal output exceeds the set sensor thermal output threshold, the sensor will stop working until the sensor thermal output is lower than the set sensor thermal output threshold. Since the sensor stops working, the data transmission of the wireless sensor network will be interrupted. However, in fact, when the sensor thermal output is too large, the wireless sensor network may be able to perform data transmission normally because the transmission energy consumption is normal. At this time, for the sensor, the sensor thermal output threshold set is unreasonable, thereby affecting the operation of the wireless sensor network. Therefore, according to the sensor thermal output threshold set in the wireless sensor network, all historical log data that exceeds the sensor thermal output threshold are found. Although the sensor will stop working, the data and related information collected by it will still be reflected in the log data. The log data is used to analyze whether the transmission energy consumption of the wireless sensor network is normal.

[0031] Step S2, predicting the transmission energy consumption of the wireless sensor network by finding the historical log data. If the predicted transmission energy consumption of the wireless sensor network exceeds the specified energy consumption threshold, it means that data transmission under the corresponding sensor thermal output will cause the transmission energy consumption in the wireless sensor network to be too high, greatly reducing the data transmission quality, that is, it means that the setting of the sensor threshold is reasonable, and there is no need to update the sensor thermal output threshold; otherwise, the sensor thermal output threshold is optimized by a genetic algorithm, and the optimal sensor thermal output threshold is found by the global search capability of the genetic algorithm. The result is more accurate, which can meet the long-term working needs of the wireless sensor network, reduce downtime, and improve the stability of the wireless sensor network.

[0032] The specific process of transmission energy consumption prediction of wireless sensor network in the present invention is as follows:

[0033] E=E s ×L+E r ×N n ×L

[0034] Where E represents the predicted value of transmission energy consumption of wireless sensor networks, E s represents the energy consumed by the sensor to send data, L represents the length of the broadcast data, and E r N represents the energy consumed by the wireless sensor network to receive data. n Represents the number of neighbor nodes. By comprehensively considering the energy consumed by data transmission and the energy consumed by data reception, the energy consumption of data in the entire wireless sensor network transmission process can be more accurately predicted, and the transmission energy consumption prediction accuracy of the wireless sensor network can be improved. Whether the transmission energy consumption of the wireless sensor network exceeds the specified energy consumption threshold is used as the basis for judging whether the setting of the sensor thermal output threshold is reasonable, which can improve the accuracy of the sensor thermal output threshold optimization.

[0035] like Figure 2 The specific process of optimizing the sensor thermal output threshold by genetic algorithm in the present invention is as follows:

[0036] Step S2.1, using the predicted value of the transmission energy consumption of the wireless sensor network as the fitness function of the genetic algorithm, randomly initializing a number of sensor thermal output thresholds as the initial population of the genetic algorithm, using the sensor thermal output thresholds in the population as individuals of the genetic algorithm, and setting the maximum number of iterations of the genetic algorithm;

[0037] Step S2.2, use all individuals to run the wireless sensor network, find all the log data that exceeds the individuals, and calculate the fitness function;

[0038] Step S2.3, retaining the individual with the lowest fitness function value, performing selection, crossover and mutation, and obtaining updated individuals, which can enhance individual diversity, improve search efficiency and convergence speed, and thus improve the quality of the optimal solution;

[0039] The specific process of crossing in the present invention is:

[0040]

[0041] Among them, Pc n represents the crossover rate of individuals in the nth iteration, k1 represents the constant factor controlling the crossover rate of individuals, F n Indicates the fitness function value of the retained individual in the nth iteration, max(F n ) represents the maximum value of the individual fitness function in the population under the nth iteration, min(F n ) represents the minimum value of the individual fitness function in the population at the nth iteration.

[0042] The specific process of variation in the present invention is:

[0043]

[0044] Among them, Pm n represents the individual mutation rate under the nth iteration, k2 represents the constant factor controlling the individual mutation rate, represents the mean value of the fitness function of all individuals in the population in the first n iterations, F min represents the minimum fitness function value of all individuals in the population in the first n iterations, min(F n ) represents the minimum value of the individual fitness function in the population at the nth iteration.

[0045] Step S2.4, repeat steps S2.2-2.3 until the maximum number of iterations is reached, and take the individual with the lowest fitness function value as the optimal sensor thermal output threshold.

[0046] The sensor thermal output threshold optimization method based on the wireless sensor network of the present invention finds out the unreasonable sensor thermal output threshold according to the predicted value of the transmission energy consumption of the wireless sensor network, thereby optimizing the unreasonable sensor thermal output threshold, making the wireless sensor network suitable for more application scenarios; and, the present invention optimizes the unreasonable sensor thermal output threshold through a genetic algorithm, and finds out the optimal sensor thermal output threshold through the global search capability of the genetic algorithm, the result is more accurate, can meet the long-term operation requirements of the wireless sensor network, reduce downtime, and improve the stability of the wireless sensor network.

[0047] In a technical solution of the present invention, a computer-readable storage medium is also provided, storing a computer program, wherein the computer program enables a computer to execute the sensor thermal output threshold optimization method based on a wireless sensor network.

[0048] In a technical solution of the present invention, an electronic device is also provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the sensor thermal output threshold optimization method based on a wireless sensor network is implemented.

[0049] In a technical solution of the present invention, a computer program product is also provided, including a computer program, and when the computer program is executed by a processor, the sensor thermal output threshold optimization method based on a wireless sensor network is implemented.

[0050] In the embodiments disclosed in the present application, the computer storage medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. The computer storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. More specific examples of computer storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0051] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0052] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A sensor thermal output threshold optimization method based on wireless sensor network, characterized in that: The specific steps include: Step S1, according to the sensor thermal output threshold value set in the wireless sensor network, find out all historical log data that exceeds the sensor thermal output threshold value; Step S2: predicting the transmission energy consumption of the wireless sensor network by finding the historical log data; if the predicted transmission energy consumption of the wireless sensor network exceeds the specified energy consumption threshold, there is no need to update the sensor thermal output threshold; otherwise, optimizing the sensor thermal output threshold by a genetic algorithm.

2. The method for optimizing the thermal output threshold of a sensor based on a wireless sensor network according to claim 1, characterized in that: The specific process of transmission energy consumption prediction of the wireless sensor network in step S2 is as follows: E=E s ×L+E r ×N n ×L Where E represents the predicted value of transmission energy consumption of wireless sensor networks, E s represents the energy consumed by the sensor to send data, L represents the length of the broadcast data, and E r N represents the energy consumed by the wireless sensor network to receive data. n Indicates the number of neighbor nodes.

3. The method for optimizing the thermal output threshold of a sensor based on a wireless sensor network according to claim 2, characterized in that: The specific process of optimizing the sensor thermal output threshold by genetic algorithm in step S2 is as follows: Step S2.1, using the predicted value of the transmission energy consumption of the wireless sensor network as the fitness function of the genetic algorithm, randomly initializing a number of sensor thermal output thresholds as the initial population of the genetic algorithm, using the sensor thermal output thresholds in the population as individuals of the genetic algorithm, and setting the maximum number of iterations of the genetic algorithm; Step S2.2, use all individuals to run the wireless sensor network, find all the log data that exceeds the individuals, and calculate the fitness function; Step S2.3, retain the individual with the lowest fitness function value, perform selection, crossover and mutation, and obtain updated individuals; Step S2.4, repeat steps S2.2-2.3 until the maximum number of iterations is reached, and take the individual with the lowest fitness function value as the optimal sensor thermal output threshold.

4. The method for optimizing the thermal output threshold of a sensor based on a wireless sensor network according to claim 3, characterized in that: The specific process of crossover in step S2.3 is: Among them, Pc n represents the crossover rate of individuals in the nth iteration, k1 represents the constant factor controlling the crossover rate of individuals, F n Indicates the fitness function value of the retained individual in the nth iteration, max(F n ) represents the maximum value of the individual fitness function in the population under the nth iteration, min(F n ) represents the minimum value of the individual fitness function in the population at the nth iteration.

5. The method for optimizing the thermal output threshold of a sensor based on a wireless sensor network according to claim 3, characterized in that: The specific process of mutation in step S2.3 is: Among them, Pm n represents the individual mutation rate under the nth iteration, k2 represents the constant factor controlling the individual mutation rate, represents the mean value of the fitness function of all individuals in the population in the first n iterations, F min represents the minimum fitness function value of all individuals in the population in the first n iterations, min(F n ) represents the minimum value of the individual fitness function in the population at the nth iteration.

6. A computer-readable storage medium storing a computer program, characterized in that: The computer program enables a computer to execute the sensor thermal output threshold optimization method based on a wireless sensor network as described in any one of claims 1 to 5.

7. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for optimizing the thermal output threshold of a sensor based on a wireless sensor network as described in any one of claims 1 to 5 is implemented.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for optimizing the thermal output threshold of a sensor based on a wireless sensor network according to any one of claims 1 to 5 is implemented.

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