Environmental protection data acquisition method and system based on GPRS (General Packet Radio Service)

Abstract

The invention discloses a GPRS-based environmental protection data acquisition method and system, and relates to the technical field of environmental protection data acquisition, and the method comprises the steps: collecting environmental data, and dynamically adjusting the sampling frequency according to the change of the environmental data; compressing the collected environment data, and adaptively adjusting the compression ratio according to the change rate of the data; compressed data are transmitted through GPRS, energy is dynamically distributed based on intelligent energy management, and the power consumption of each device is regulated and controlled. According to the method, the bandwidth and the compression ratio are dynamically adjusted, transmission can be optimized according to the real-time network condition, bandwidth resources are fully utilized, and resource waste or overload is avoided. By monitoring the bandwidth in real time and dynamically adjusting the size of the data block, the bandwidth utilization rate is improved, and the problem of bandwidth waste in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental data acquisition, and in particular to a GPRS-based environmental data acquisition method and system. Background Art

[0002] In the operation of modern thermal power plants, the online monitoring system of water pollution sources has become an important part of environmental protection supervision. With the continuous improvement of environmental protection standards, power plants need to monitor the wastewater generated in their production process in real time to ensure that the pollutant content in the discharge meets the requirements of national and local environmental protection regulations. The online monitoring system of water pollution sources provides timely and accurate data support by monitoring various pollutants in wastewater (such as heavy metals, suspended particulate matter, chemical oxygen demand, etc.) to ensure the compliance and environmental protection of wastewater treatment.

[0003] Existing online monitoring systems for water pollution sources usually rely on sensors, data acquisition devices and network transmission technology to transmit pollutant data collected at each monitoring point to a central processing unit for analysis. However, with the increase in monitoring needs and the expansion of monitoring areas, online monitoring systems for water pollution sources face problems in data transmission efficiency, stability and network resource utilization. The existing technology has many defects in the data block transmission and optimization process. Common problems include insufficient bandwidth utilization, especially when the network bandwidth is high, the system cannot dynamically adjust the size of the transmission data block, resulting in bandwidth waste. The compression rate of the existing system is not adjusted enough, most of which use static compression rates, and cannot be dynamically adjusted according to real-time network conditions, data types, etc., resulting in low transmission efficiency. At the same time, the processing capacity of network delay is also limited. The current fixed strategy cannot adapt to network congestion or large delay fluctuations, which in turn causes transmission interruption or time increase. The existing technology has poor response capabilities to periodic network fluctuations, and cannot effectively predict and adjust, resulting in reduced transmission performance in a dynamic bandwidth allocation environment. In response to these shortcomings, we have invented a GPRS-based environmental data acquisition method and system. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above problems or problems existing in the prior art, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is that when the network bandwidth is high, the system cannot dynamically adjust the size of the transmission data block, resulting in bandwidth waste. The compression rate of the existing system is insufficient, and most of them use static compression rate, which cannot be dynamically adjusted according to the real-time network status, data type, etc., resulting in low transmission efficiency.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a GPRS-based environmental data acquisition method, which includes collecting environmental data and dynamically adjusting the sampling frequency according to changes in the environmental data;

[0008] Compress the collected environmental data and adaptively adjust the compression ratio according to the rate of change of the data;

[0009] The compressed data is transmitted via GPRS, and energy is dynamically allocated based on intelligent energy management to regulate the power consumption of each device.

[0010] As a preferred solution of the GPRS-based environmental data acquisition method of the present invention, the dynamic adjustment of the sampling frequency includes:

[0011] Collect environmental data in real time through sensors;

[0012] The environmental data includes temperature, humidity and signal strength;

[0013] Each environmental data is standardized and expressed as follows:

[0014]

[0015]

[0016]

[0017] Where D1(t) is the original temperature data at the current time t; is the standardized temperature data;

[0018] D1,max and D1,min are the maximum and minimum values ​​of temperature respectively; p is the nonlinear adjustment index of temperature; D2(t) is the original humidity data at the current time t; is the standardized humidity data; D 2,max and D 2,min is the maximum and minimum value of humidity; φ is the humidity adjustment coefficient; D3(t) is the original signal strength data at the current time t;

[0019] is the normalized signal intensity data; D 3,max is the maximum value of signal strength; ζ is the amplification factor of signal strength;

[0020] Multi-source data fusion is performed based on the standardized data, which is expressed as:

[0021]

[0022] Where D(t) is the environmental data at the current moment; q1, q2, and q3 are the nonlinear adjustment indexes of temperature, humidity, and signal strength, respectively;

[0023] Calculate the difference between the current environmental data and the environmental data at the previous moment to get the rate of change of the environmental data, expressed as:

[0024] ΔD(t)=|D(t)-D(t-1)|

[0025] Where D(t) is the environmental data at the current moment, D(t-1) is the environmental data at the previous moment; ΔD(t) is the rate of change of environmental data

[0026] The sampling frequency is adjusted based on the rate of change of environmental data. The adjustment formula is:

[0027]

[0028] Among them, f(t+1) is the sampling frequency at time t+1, f(t) is the sampling frequency at time t, D(t) is the environmental data at time t, D(t-1) is the environmental data at the previous moment, α is the influence coefficient of the current data change rate, β is the sensitivity index of the data change rate, and j is the smoothing adjustment coefficient.

[0029] As a preferred solution of the GPRS-based environmental data acquisition method of the present invention, the dynamic adjustment of the sampling frequency also includes setting a smooth transition mechanism to avoid frequent changes in the sampling frequency due to short-term fluctuations;

[0030] The smooth transition mechanism includes:

[0031] Assume D avg is the historical average environmental data, then the environmental data fluctuation at the current moment is

[0032] |D(t)-D avg ∣;

[0033] Construct the loss function of sampling frequency change, expressed as:

[0034]

[0035] Among them, L1(f) is the loss function;

[0036] The deviation control term that sets the reference sampling frequency is expressed as:

[0037]

[0038] Wherein, L2(f) is the deviation control term for the reference sampling frequency;

[0039] Combining the two loss terms, a smooth loss function is constructed, which is expressed as:

[0040]

[0041] Among them, L(f) is the total value of the smooth loss function, f(t) is the sampling frequency at time t, f(t+1) is the sampling frequency at the next time point, κ is the control coefficient of the sampling frequency change, μ is the adjustment coefficient of the influence of data fluctuation on the sampling frequency, η is the exponential decay rate, D(t) is the environmental data at the current moment, and D avg is the historical average value of environmental data, λ is the reference sampling frequency deviation control coefficient, f ref is the reference sampling frequency, μ is the frequency deviation adjustment coefficient, ω is the sine period adjustment parameter, t i is the time point, n is the number of sampling points, sin 2 (ω·t i ) is the sine function modulation term, and T is the total time of the sampling duration.

[0042] As a preferred solution of the GPRS-based environmental data acquisition method of the present invention, the compressed collected environmental data and the adaptively adjusted compression ratio according to the data change rate include:

[0043] Preprocess the collected environmental data and divide it into multiple data blocks;

[0044] Calculate the change rate ΔA(t) of each data block and adjust the compression ratio C(t) according to the change rate. The adjustment formula is:

[0045]

[0046] Among them, C(t) is the compression ratio at the current moment, ΔA(t) is the data change rate, and A t is the preset change threshold;

[0047] The data is compressed based on the adjusted compression ratio, and the compressed data is prepared for transmission.

[0048] As a preferred solution of the GPRS-based environmental data acquisition method of the present invention, wherein: the compressed collected environmental data and the adaptively adjusted compression ratio according to the data change rate also include:

[0049] Calculate the size of all data blocks before transmission to determine the final transmission data volume of each data block;

[0050] The optimization goal of data transmission volume is:

[0051]

[0052] Among them, T f For the final optimized transmission data volume, B i is the size of the i-th data block, α is the nonlinear adjustment parameter of the data block size, C i is the compression ratio of the ith data block, ∈ is the protection constant to avoid the denominator being zero, R i is the transmission bandwidth of the ith data block, L i is the network delay of the ith data block, γ is the adjustment coefficient of the delay effect, β is the nonlinear effect of controlling the delay, ζ is the adjustment coefficient of the transmission efficiency, tanh is the smooth adjustment function, ψ is the environmental data adjustment coefficient, and D i is the environmental data of the ith data block, δ is the nonlinear index that controls the impact of environmental data on transmission, and m is the total number of data blocks.

[0053] As a preferred solution of the GPRS-based environmental data acquisition method of the present invention, the dynamic allocation of energy based on intelligent energy management includes:

[0054] Monitor the energy consumption of sampling equipment, compression equipment and transmission equipment in real time, and record the power consumption of each device separately;

[0055] Energy is dynamically allocated according to the working status and task load of each device. The energy allocation of sampling devices is increased at high sampling frequencies, and the energy allocation of compression and transmission devices is increased at high data volumes.

[0056] As a preferred solution of the GPRS-based environmental data acquisition method of the present invention, wherein: the power consumption of each device is regulated,

[0057] Monitor the remaining power of sensor nodes in real time and dynamically adjust power consumption based on the remaining power and the current energy consumption of each device;

[0058] When the remaining power is lower than a preset threshold, the operating frequency of the sampling device and the compression device is reduced;

[0059] Adjust transmission frequency, prioritize critical data, and control device operating time based on energy status.

[0060] Another object of the present invention is to provide a GPRS-based environmental data acquisition system, which can collect environmental data and dynamically adjust the sampling frequency according to changes in the environmental data; compress the collected environmental data and adaptively adjust the compression ratio according to the rate of change of the data; transmit the compressed data through GPRS, and dynamically allocate energy based on intelligent energy management to regulate the power consumption of each device, thereby solving the problem that the current GPRS-based environmental data acquisition is difficult to adapt to complex scenarios.

[0061] As a preferred solution of the GPRS-based environmental data acquisition system of the present invention, it includes: a collection and storage module, a compression module, and an adjustment module;

[0062] The acquisition and storage module is used to collect environmental data and dynamically adjust the sampling frequency according to changes in environmental data;

[0063] The power consumption prediction module is used to compress the collected environmental data and adaptively adjust the compression ratio according to the rate of change of the data;

[0064] The real-time allocation module is used to transmit compressed data via GPRS, dynamically allocate energy based on intelligent energy management, and regulate the power consumption of each device.

[0065] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a step of a GPRS-based environmental protection data acquisition method.

[0066] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a GPRS-based environmental protection data acquisition method.

[0067] Beneficial effects of the present invention: The present invention can optimize transmission according to real-time network conditions by dynamically adjusting bandwidth and compression rate, fully utilize bandwidth resources, and avoid resource waste or overload. By real-time monitoring of bandwidth and dynamically adjusting the size of data blocks, bandwidth utilization is improved, overcoming the problem of bandwidth waste in the prior art. The adaptive adjustment mechanism of the compression rate improves the inefficiency caused by the fixed compression rate and improves transmission efficiency. The adaptive delay processing mechanism enables the system to flexibly adjust the transmission mode according to changes in network delays, reducing the impact of delays on transmission efficiency. For complex environmental interference, the present invention effectively ensures the stability of transmission through real-time analysis of environmental data and transmission optimization, avoiding instability caused by environmental influences in the prior art. By coping with periodic network fluctuations, the present invention solves the problem of poor adaptability to dynamic network environments in the prior art, ensuring efficient transmission during network fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0069] Figure 1 This is the overall flow chart of the environmental data acquisition method based on GPRS.

[0070] Figure 2 This is the overall flow chart of the environmental data acquisition system based on GPRS.

[0071] Figure 3 It is a schematic diagram of the overall computer equipment. DETAILED DESCRIPTION

[0072] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0073] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0074] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0075] Example 1

[0076] Reference Figures 1 to 3 , as an embodiment of the present invention, provides a method for acquiring environmental protection data based on GPRS, comprising:

[0077] S1: Collect environmental data and dynamically adjust the sampling frequency according to changes in environmental data.

[0078] Furthermore, the dynamic adjustment of the sampling frequency includes:

[0079] Collect environmental data in real time through sensors;

[0080] The environmental data includes temperature, humidity and signal strength;

[0081] Each environmental data is standardized and expressed as follows:

[0082]

[0083]

[0084]

[0085] Where D1(t) is the original temperature data at the current time t; is the standardized temperature data; D1,max and D1,min are the maximum and minimum values ​​of temperature respectively; p is the nonlinear adjustment index of temperature; D2(t) is the original humidity data at the current time t; is the standardized humidity data; D 2,max and D 2,min is the maximum and minimum value of humidity; φ is the humidity adjustment coefficient; D3(t) is the original signal strength data at the current time t;

[0086] is the normalized signal intensity data; D 3,max is the maximum value of signal strength; ζ is the amplification factor of signal strength;

[0087] Multi-source data fusion is performed based on the standardized data, which is expressed as:

[0088]

[0089] Where D(t) is the environmental data at the current moment; q1, q2, and q3 are the nonlinear adjustment indexes of temperature, humidity, and signal strength, respectively;

[0090] Calculate the difference between the current environmental data and the environmental data at the previous moment to get the rate of change of the environmental data, expressed as:

[0091] ΔD(t)=|D(t)-D(t-1)|

[0092] Wherein, D(t) is the environmental data at the current moment, D(t-1) is the environmental data at the previous moment; ΔD(t) is the rate of change of the environmental data.

[0093] It should be noted that D(t) represents the environmental data at the current time t, which is a key variable affecting the adjustment of data transmission. Environmental data is usually a variety of parameters related to the data transmission environment, which will affect the stability, bandwidth, delay and other network performance of the transmission.

[0094] The sampling frequency is adjusted based on the rate of change of environmental data. The adjustment formula is:

[0095]

[0096] Among them, f(t+1) is the sampling frequency at time t+1, f(t) is the sampling frequency at time t, D(t) is the environmental data at time t, D(t-1) is the environmental data at the previous moment, α is the influence coefficient of the current data change rate, β is the sensitivity index of the data change rate, and j is the smoothing adjustment coefficient.

[0097] It should be noted that the mechanism of dynamically adjusting the sampling frequency enables the data collection process to adaptively adjust resource input according to environmental changes, achieving a balance between efficient data acquisition and resource conservation. Compared with the traditional fixed sampling frequency strategy, by combining the changing characteristics of environmental data, the adaptability to complex environments is improved. Under the condition that data transmission and power consumption are strictly restricted, by dynamically adjusting the sampling frequency, the working life can be extended and the overall efficiency can be improved without affecting the data quality.

[0098] Furthermore, the dynamic adjustment of the sampling frequency also includes setting a smooth transition mechanism to avoid frequent changes in the sampling frequency due to short-term fluctuations;

[0099] The smooth transition mechanism includes:

[0100] Assume D avg is the historical average environmental data, then the environmental data fluctuation at the current moment is

[0101] |D(t)-D avg ∣;

[0102] Construct the loss function of sampling frequency change, expressed as:

[0103]

[0104] Among them, L1(f) is the loss function;

[0105] The deviation control term that sets the reference sampling frequency is expressed as:

[0106]

[0107] Wherein, L2(f) is the deviation control term for the reference sampling frequency;

[0108] Combining the two loss terms, a smooth loss function is constructed, which is expressed as:

[0109]

[0110] Among them, L(f) is the total value of the smooth loss function, f(t) is the sampling frequency at time t, f(t+1) is the sampling frequency at the next time point, κ is the control coefficient of the sampling frequency change, μ is the adjustment coefficient of the influence of data fluctuation on the sampling frequency, η is the exponential decay rate, D(t) is the environmental data at the current moment, and D avg is the historical average value of environmental data, λ is the reference sampling frequency deviation control coefficient, f ref is the reference sampling frequency, μ is the frequency deviation adjustment coefficient, ω is the sine period adjustment parameter, t i is the time point, n is the number of sampling points, sin 2 (ω·t i ) is the sine function modulation term, and T is the total time of the sampling duration.

[0111] It should be noted that, combined with the mechanism of dynamically adjusting the sampling frequency, the sampling frequency is optimized by smoothing the loss function, which solves the problem of sampling frequency adjustment caused by frequent fluctuations and ensures data transmission efficiency and stability. The former avoids drastic fluctuations in the sampling frequency through smoothing, and the latter controls the degree to which the sampling frequency deviates from the reference frequency, ensuring that the system does not frequently adjust the frequency while maintaining high-precision acquisition.

[0112] S2: compress the collected environmental data and adaptively adjust the compression ratio according to the rate of change of the data.

[0113] Furthermore, compressing the collected environmental data and adaptively adjusting the compression ratio according to the rate of change of the data includes:

[0114] Preprocess the collected environmental data and divide it into multiple data blocks;

[0115] Calculate the change rate ΔA(t) of each data block and adjust the compression ratio C(t) according to the change rate. The adjustment formula is:

[0116]

[0117] Among them, C(t) is the compression ratio at the current moment, ΔA(t) is the data change rate, and A t is the preset change threshold;

[0118] The data is compressed based on the adjusted compression ratio, and the compressed data is prepared for transmission.

[0119] Furthermore, compressing the collected environmental data and adaptively adjusting the compression ratio according to the rate of change of the data also includes:

[0120] Calculate the size of all data blocks before transmission to determine the final transmission data volume of each data block;

[0121] The optimization goal of data transmission volume is:

[0122]

[0123] Among them, T f For the final optimized transmission data volume, B i is the size of the i-th data block, α is the nonlinear adjustment parameter of the data block size, C i is the compression ratio of the ith data block, ∈ is the protection constant to avoid the denominator being zero, R i is the transmission bandwidth of the ith data block, L i is the network delay of the ith data block, γ is the adjustment coefficient of the delay effect, β is the nonlinear effect of controlling the delay, ζ is the adjustment coefficient of the transmission efficiency, tanh is the smooth adjustment function, ψ is the environmental data adjustment coefficient, and D i is the environmental data of the ith data block, δ is the nonlinear index that controls the impact of environmental data on transmission, and m is the total number of data blocks.

[0124] It should be noted that after collecting environmental data, the compression ratio C is adaptively adjusted according to the data change rate. i , effectively reducing the transmission size of data blocks. When network bandwidth is limited, compression can significantly improve transmission efficiency, reduce bandwidth requirements, and maintain the integrity of transmitted data. Before each data transmission, the system calculates the size of all data blocks. And according to the current network status (bandwidth R i and delay L i ) adjusts the transmission amount of the data block. By optimizing the size of each data block, the transmission load can be evenly distributed under different network conditions to avoid network congestion; through the smoothing function, the system can smoothly adjust the transmission rate and data block size when facing environmental fluctuations and delay changes. The advantage of this mechanism is that it prevents the transmission efficiency from fluctuating due to short-term drastic changes in environmental data, ensuring the stability and continuity of data transmission. Optimize the transmission objective function T f By combining data block size, compression rate, bandwidth and environmental data, a data acquisition method for dynamic adjustment and optimization of transmission is formed. The multivariable optimization model can not only adapt to different network environments in real time, but also ensure the stability of transmission through a smoothing mechanism. By balancing data compression, network delay and bandwidth limitations through a mathematical model, the system can efficiently transmit data under complex network conditions, improve data transmission efficiency and reduce resource consumption.

[0125] Furthermore, if the amount of data transmitted is extremely low (T f≈0), the bandwidth can be fully utilized by appropriately increasing the size of the data block or performing batch transmission; when the bandwidth utilization rate is lower than the set threshold, the bandwidth status should be monitored in real time, multiple data blocks should be merged for transmission, and the compression algorithm should be adjusted to reduce the data processing burden.

[0126] If the amount of data to be transmitted is medium (the first threshold > T f >0, keep it moderate), the current transmission rhythm should be maintained and the network status should be continuously monitored; when the bandwidth and latency are within the normal range, the system should regularly detect their fluctuations, transmit data according to the preset block size, and dynamically adjust the compression ratio during the transmission process to adapt to network changes and task timeliness.

[0127] If the amount of data to be transmitted is large (T f > the first threshold), it is necessary to immediately increase the compression rate and reduce the data block size to reduce network bandwidth occupancy; when the bandwidth bottleneck is obvious and the network delay is too high, the transmission of key data should be ensured by reducing the data block size and adjusting the priority, while continuously monitoring the network status and flexibly adjusting the transmission strategy.

[0128] If the environmental interference is serious, resulting in a significant increase in the amount of transmitted data, the environmental adaptability should be optimized, and the transmission time should be adjusted or a redundancy mechanism should be introduced to improve the reliability of the transmission. i When the interference is large, the system should select a low-interference period for data transmission, and ensure data integrity through redundancy check or repeated transmission, appropriately reduce the transmission speed and try to switch to a more stable channel.

[0129] If the transmission volume fluctuates periodically, bandwidth allocation and load balancing should be adjusted dynamically according to the fluctuations. When the bandwidth peak period arrives, bandwidth resources should be reserved in advance by predicting bandwidth fluctuations, and transmission tasks should be slowed down or suspended during peak hours. Distributed transmission can be used to reduce the burden on a single link and avoid network congestion.

[0130] S3: Transmit compressed data via GPRS, dynamically allocate energy based on intelligent energy management, and regulate the power consumption of each device.

[0131] Furthermore, the dynamic allocation of energy based on intelligent energy management includes:

[0132] Monitor the energy consumption of sampling equipment, compression equipment and transmission equipment in real time, and record the power consumption of each device separately;

[0133] Energy is dynamically allocated according to the working status and task load of each device. The energy allocation of sampling devices is increased at high sampling frequencies, and the energy allocation of compression and transmission devices is increased at high data volumes.

[0134] Furthermore, the regulating the power consumption of each device includes:

[0135] Monitor the remaining power of sensor nodes in real time and dynamically adjust power consumption based on the remaining power and the current energy consumption of each device;

[0136] When the remaining power is lower than a preset threshold, the operating frequency of the sampling device and the compression device is reduced;

[0137] Adjust transmission frequency, prioritize critical data, and control device operating time based on energy status.

[0138] In summary, the present invention can optimize transmission according to real-time network conditions by dynamically adjusting bandwidth and compression rate, make full use of bandwidth resources, and avoid resource waste or overload. By real-time monitoring of bandwidth and dynamically adjusting the size of data blocks, the bandwidth utilization rate is improved, and the problem of bandwidth waste in the prior art is overcome. The adaptive adjustment mechanism of the compression rate improves the inefficiency caused by the fixed compression rate and improves the transmission efficiency. The adaptive delay processing mechanism enables the system to flexibly adjust the transmission mode according to changes in network delays, reducing the impact of delays on transmission efficiency. For complex environmental interference, the present invention effectively ensures the stability of transmission through real-time analysis and transmission optimization of environmental data, avoiding the instability caused by environmental influences in the prior art. By coping with periodic network fluctuations, the present invention solves the problem of poor adaptability to dynamic network environments in the prior art, ensuring efficient transmission during network fluctuations.

[0139] The above is a schematic scheme of a GPRS-based environmental data acquisition method of this embodiment. It should be noted that the technical scheme of the GPRS-based environmental data acquisition system and the technical scheme of the GPRS-based environmental data acquisition method belong to the same concept. For details not described in detail in the technical scheme of the GPRS-based environmental data acquisition system in this embodiment, please refer to the description of the technical scheme of the GPRS-based environmental data acquisition method.

[0140] This embodiment also proposes a GPRS-based environmental data acquisition system, including:

[0141] Collection and storage module, compression module, adjustment module;

[0142] The acquisition and storage module is used to collect environmental data and dynamically adjust the sampling frequency according to changes in environmental data;

[0143] The power consumption prediction module is used to compress the collected environmental data and adaptively adjust the compression ratio according to the rate of change of the data;

[0144] The real-time allocation module is used to transmit compressed data via GPRS, dynamically allocate energy based on intelligent energy management, and regulate the power consumption of each device.

[0145] This embodiment further provides a computing device, which is applicable to the case of obtaining environmental protection data based on GPRS, including:

[0146] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the GPRS-based environmental protection data acquisition method proposed in the above embodiment.

[0147] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for acquiring environmental protection data based on GPRS as proposed in the above embodiment is implemented.

[0148] The storage medium proposed in this embodiment and the method for implementing GPRS-based environmental data acquisition proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0149] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0150] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0151] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0152] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0153] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0154] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for acquiring environmental protection data based on GPRS, characterized in that: include, Collect environmental data and dynamically adjust the sampling frequency according to changes in environmental data; Compress the collected environmental data and adaptively adjust the compression ratio according to the rate of change of the data; The compressed data is transmitted via GPRS, and energy is dynamically allocated based on intelligent energy management to regulate the power consumption of each device.

2. The GPRS-based environmental data acquisition method according to claim 1, characterized in that: The dynamic adjustment of the sampling frequency includes: Collect environmental data in real time through sensors; The environmental data includes temperature, humidity and signal strength; Each environmental data is standardized and expressed as follows: Where D1(t) is the original temperature data at the current time t; is the standardized temperature data; D1,max and D1,min are the maximum and minimum values ​​of temperature respectively; p is the nonlinear adjustment index of temperature; D2(t) is the original humidity data at the current time t; is the standardized humidity data; D 2,max and D 2,min is the maximum and minimum value of humidity; φ is the humidity adjustment coefficient; D3(t) is the original signal strength data at the current time t; is the normalized signal intensity data; D 3,max is the maximum value of signal strength; ζ is the amplification factor of signal strength; Multi-source data fusion is performed based on the standardized data, which is expressed as: Where D(t) is the environmental data at the current moment; q1, q2, and q3 are the nonlinear adjustment indexes of temperature, humidity, and signal strength, respectively; Calculate the difference between the current environmental data and the environmental data at the previous moment to get the rate of change of the environmental data, expressed as: ΔD(t)=|D(t)-D(t-1)| Where D(t-1) is the environmental data at the previous moment; ΔD(t) is the rate of change of environmental data; The sampling frequency is adjusted based on the rate of change of environmental data. The adjustment formula is: Among them, f(t+1) is the sampling frequency at time t+1, f(t) is the sampling frequency at time t, D(t) is the environmental data at time t, D(t-1) is the environmental data at the previous moment, α is the influence coefficient of the current data change rate, β is the sensitivity index of the data change rate, and j is the smoothing adjustment coefficient.

3. The GPRS-based environmental data acquisition method according to claim 2, characterized in that: The dynamic adjustment of the sampling frequency also includes setting a smooth transition mechanism to avoid frequent changes in the sampling frequency due to short-term fluctuations; The smooth transition mechanism includes: Assume D avg is the historical average environmental data, then the environmental data fluctuation at the current moment is |D(t)-D avg ∣; Construct the loss function of sampling frequency change, expressed as: Among them, L1(f) is the loss function; The deviation control term that sets the reference sampling frequency is expressed as: Wherein, L2(f) is the deviation control term for the reference sampling frequency; Combining the two loss terms, a smooth loss function is constructed, which is expressed as: Among them, L(f) is the total value of the smooth loss function, κ is the control coefficient of the sampling frequency change, μ is the adjustment coefficient of the influence of data fluctuation on the sampling frequency, η is the exponential decay rate, D(t) is the environmental data at the current moment, and D avg is the historical average value of environmental data, λ is the reference sampling frequency deviation control coefficient, f ref is the reference sampling frequency, μ is the frequency deviation adjustment coefficient, ω is the sine period adjustment parameter, t i is the time point, n is the number of sampling points, and T is the total time of the sampling duration.

4. The GPRS-based environmental data acquisition method according to claim 3, characterized in that: The compressing of the collected environmental data and adaptively adjusting the compression ratio according to the rate of change of the data includes: Preprocess the collected environmental data and divide it into multiple data blocks; Calculate the change rate ΔA(t) of each data block and adjust the compression ratio C(t) according to the change rate. The adjustment formula is: Among them, C(t) is the compression ratio at the current moment, ΔA(t) is the data change rate, and A t is the preset change threshold; The data is compressed based on the adjusted compression ratio, and the compressed data is prepared for transmission.

5. The environmental protection data acquisition method based on GPRS as claimed in claim 4, characterized in that: The compressing the collected environmental data and adaptively adjusting the compression ratio according to the rate of change of the data also includes: Calculate the size of all data blocks before transmission to determine the final transmission data volume of each data block; The optimization goal of data transmission volume is: Among them, T f For the final optimized transmission data volume, B i is the size of the i-th data block, α is the nonlinear adjustment parameter of the data block size, C i is the compression ratio of the ith data block, ∈ is the protection constant to avoid the denominator being zero, R i is the transmission bandwidth of the ith data block, L i is the network delay of the ith data block, γ is the adjustment coefficient of the delay effect, β is the nonlinear effect of controlling the delay, ζ is the adjustment coefficient of the transmission efficiency, tanh is the smooth adjustment function, ψ is the environmental data adjustment coefficient, and D i is the environmental data of the ith data block, δ is the nonlinear index that controls the impact of environmental data on transmission, and m is the total number of data blocks.

6. The GPRS-based environmental data acquisition method according to claim 5, characterized in that: The dynamic allocation of energy based on intelligent energy management includes: Monitor the energy consumption of sampling equipment, compression equipment and transmission equipment in real time, and record the power consumption of each device separately; Energy is dynamically allocated according to the working status and task load of each device. The energy allocation of sampling devices is increased at high sampling frequencies, and the energy allocation of compression and transmission devices is increased at high data volumes.

7. The GPRS-based environmental data acquisition method according to claim 6, characterized in that: The controlling of the power consumption of each device includes: Monitor the remaining power of sensor nodes in real time and dynamically adjust power consumption based on the remaining power and the current energy consumption of each device; When the remaining power is lower than a preset threshold, the operating frequency of the sampling device and the compression device is reduced; Adjust transmission frequency, prioritize critical data, and control device operating time based on energy status.

8. A system using the GPRS-based environmental data acquisition method as claimed in any one of claims 1 to 7, characterized in that: Including collection and storage module, compression module and adjustment module; The acquisition and storage module is used to collect environmental data and dynamically adjust the sampling frequency according to changes in environmental data; The power consumption prediction module is used to compress the collected environmental data and adaptively adjust the compression ratio according to the rate of change of the data; The real-time allocation module is used to transmit compressed data via GPRS, dynamically allocate energy based on intelligent energy management, and regulate the power consumption of each device.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the GPRS-based environmental protection data acquisition method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the GPRS-based environmental protection data acquisition method according to any one of claims 1 to 7 are implemented.

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