CN114726380B - Monitoring data lossless compression method, device, equipment and readable storage medium - Google Patents

Abstract

The invention provides a monitoring data lossless compression method, a monitoring data lossless compression device, monitoring data lossless compression equipment and a readable storage medium, which relate to the technical field of data calculation and comprise the steps of obtaining a data table to be compressed; calculating all time data according to a preset time difference mathematical model to obtain a timestamp compression result; calculating all monitoring values according to a preset data system conversion mathematical model to obtain a numerical value to be compressed; and calculating the numerical value to be compressed according to a preset Huffman coding mathematical model to obtain a monitoring value compression result. The method has the advantages that the algorithm coding including the difference idea is adopted to combine the coding ideas of code conversion, system conversion coding and Huffman coding according to the floating point number characteristic of the monitored value, the lossless compression algorithm model of the monitored value is finally constructed, the compression coding of the monitored value is completed, and the effects of improving the data compression rate and reducing the storage pressure of a database are achieved.

Description

A lossless compression method, device, device and readable storage medium for monitoring data

technical field

The present invention relates to the technical field of data computing, and in particular, to a lossless compression method, device, device and readable storage medium for monitoring data.

Background technique

As the country attaches great importance to the safety of traffic structures, the density of sensors arranged in various corners of traffic structures will become higher and higher, and the corresponding monitoring frequency will also increase. As much as PB level. At present, in the structural health monitoring of traffic structures, the structural response data of traffic structures collected by sensors installed in specific key parts of traffic structures belong to the category of massive data. workload is increasing. The current compression technology has the disadvantage of low compression rate when dealing with the monitoring data generated in the traffic structure monitoring system.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a lossless compression method, device, device and readable storage medium for monitoring data, so as to improve the above problems. In order to achieve the above object, the technical scheme adopted by the present invention is as follows:

In a first aspect, the present application provides a method for lossless compression of monitoring data, including: acquiring a data table to be compressed, where the data table to be compressed includes monitoring values collected by sensors and time data corresponding to each monitoring value, and each Each of the monitoring values is collected by the sensor in chronological order; all the time data are calculated according to a preset time difference mathematical model to obtain a timestamp compression result, and the timestamp compression result includes a start timestamp and The time stamp secondary difference value obtained after all the time data is processed by the secondary difference method; according to the preset data conversion mathematical model, all the monitoring values are calculated to obtain the value to be compressed; according to the preset hash value The Fuman coding mathematical model calculates the to-be-compressed value to obtain the monitoring value compression result.

In a second aspect, the present application also provides a device for lossless compression of monitoring data, including: a data acquisition module for acquiring a data table to be compressed, where the data table to be compressed includes monitoring values collected by sensors and each of the monitoring values. The time data corresponding to the value; the first calculation module is used to calculate all the time data according to the preset time difference mathematical model to obtain a time stamp compression result, and the time stamp compression result includes a start time stamp and all all the time stamps. The time stamp secondary difference value obtained after the time data is processed by the secondary difference method; the second calculation module is used for calculating all the monitoring values according to a preset data-to-system conversion mathematical model to obtain the to-be-compressed value; The third calculation module is configured to calculate the to-be-compressed value according to a preset Huffman coding mathematical model to obtain a monitoring value compression result.

In a third aspect, the present application also provides a lossless compression device for monitoring data, including:

memory for storing computer programs;

The processor is configured to implement the steps of the monitoring data lossless compression method when executing the computer program.

In a fourth aspect, the present application further provides a readable storage medium, where a computer program is stored on the readable storage medium, and when the computer program is executed by a processor, the steps of the above monitoring data-based lossless compression method are implemented.

The beneficial effects of the present invention are:

According to the floating-point number characteristic of the monitoring value, the present invention adopts the algorithm coding containing the difference idea to carry out coding conversion, binary conversion coding and Huffman coding, and finally constructs a lossless compression algorithm model of the monitoring value, and completes the monitoring process. The compression encoding of the value can improve the data compression rate and reduce the storage pressure of the database.

Other features and advantages of the present invention will be set forth in the description which follows, and, in part, will be apparent from the description, or may be learned by practice of embodiments of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description, claims, and drawings.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present invention more clearly, the following briefly introduces the accompanying drawings used in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore do not It should be regarded as a limitation of the scope, and for those of ordinary skill in the art, other related drawings can also be obtained according to these drawings without any creative effort.

1 is a schematic flowchart of a method for lossless compression of monitoring data described in an embodiment of the present invention;

2 is a schematic structural diagram of a monitoring data lossless compression device described in an embodiment of the present invention;

3 is a schematic structural diagram of a monitoring data lossless compression device described in an embodiment of the present invention;

FIG. 4 is a schematic diagram of ASCII code conversion described in the embodiment of the present invention.

Labels in the figure: 1, data acquisition module; 2, first calculation module; 21, first calculation unit; 22, second calculation unit; 3, second calculation module; 31, third calculation unit; 311, first judgment unit; 312, the second judgment unit; 313, the third judgment unit; 32, the fourth calculation unit; 321, the fifth calculation unit; 322, the sixth calculation unit; 4, the third calculation module; 41, the seventh calculation unit 42, eighth computing unit; 43, ninth computing unit; 800, monitoring data lossless compression device; 801, processor; 802, memory; 803, multimedia component; 804, I/O interface;

Detailed ways

In order to make the purposes, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments These are some embodiments of the present invention, but not all embodiments. The components of the embodiments of the invention generally described and illustrated in the drawings herein may be arranged and designed in a variety of different configurations. Thus, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but is merely representative of selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

It should be noted that like numerals and letters refer to like items in the following figures, so once an item is defined in one figure, it does not require further definition and explanation in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

Example 1:

This embodiment provides a lossless compression method for monitoring data.

Referring to FIG. 1 , it is shown that the method includes step S100 , step S200 , step S300 and step S400 .

Step S100: Obtain a data table to be compressed, the data table to be compressed includes monitoring values collected by the sensor and time data corresponding to each monitoring value, and each monitoring value is collected by the sensor in a chronological order.

It should be noted that, in step S100, the time series data generated by the sensor equipment in the traffic structure is collected, and the collected monitoring data is subjected to operations such as missing value filling, data standardization, and outlier elimination, and the obtained data is sorted into pending data. Compress the data table, simplify the steps of the subsequent compression process, and improve the efficiency of data processing.

S200. Calculate all time data according to a preset time difference mathematical model to obtain a time stamp compression result, where the time stamp compression result includes a starting time stamp and a second time stamp obtained by processing all time data by a quadratic difference method difference.

It should be noted that, in step S200, this step is to perform lossless compression on the time data, and after the time data is converted into a timestamp format, the timestamp compression result is obtained by performing a second difference on the timestamp data. By compressing the time data and the monitoring value separately, the calculation amount of the compressed data is reduced.

S300. Calculate all the monitored values according to a preset mathematical model of conversion of data to system to obtain the value to be compressed.

It should be noted that, in step S300, the mathematical model of data binary conversion performs multiple binary conversion on the monitoring value in the form of a floating point number, and finally converts it into ASCII code, which effectively reduces the storage cost.

S400. Calculate the value to be compressed according to a preset Huffman coding mathematical model to obtain a compression result of the monitoring value.

It should be noted that, in step S400, the Huffman coding mathematical model constructs the Huffman coding number by assuming the occurrence frequency of the ASCII code symbol in the to-be-compressed value, and greatly reduces the storage cost by storing the Huffman coding number.

In the specific embodiment disclosed in this application, step S200 includes step S210 and step S220.

S210 , converting the time data into a UNIX timestamp format and arranging in a chronological order to obtain a timestamp data column.

S220. Calculate the timestamp data column according to the preset timestamp quadratic difference method mathematical model to obtain the start timestamp and the timestamp quadratic difference.

It should be noted that, as shown in Table 1, Table 1 shows the time stamp quadratic difference method calculation table, this application converts time data into UNIX time stamp data, and extracts the starting time stamp after arranging the time stamp data The data is stored, and the rest of the time stamp data uses the difference method to perform a second difference to ensure that the number of more than 90% of the second difference is 0 and can be stored with 1 bit. The jump points and shortcomings are based on the previous Missing value padding can also ensure that the quadratic difference is within [-60,60]. This type of quadratic difference is marked with the control bit '1' and used to store 7bit D _N -D _N-1 , in the timestamp The 6 timestamps in the quadratic difference method calculation table only need 64bit+8*2+3*1=83bit for storage, which is larger than the storage cost of directly storing the timestamp (long integer is 8 bytes 64bit*6) The magnitude is reduced.

Table 1 Time stamp quadratic difference method calculation table

In the specific embodiment disclosed in this application, step S300 includes step S310 and step S320.

S310. Calculate all monitoring values according to a preset differential coding mathematical model to obtain a monitoring value code, and the monitoring value code is a decimal code.

In the specific embodiment disclosed in this application, step S310 includes step S311, step S312 and step S313.

S311 , traverse all monitoring values, perform difference calculation on two adjacent monitoring values according to the time sequence, and determine whether the calculation result is 0.

S312. If the difference between the adjacent monitoring values is 0, store the data of the monitoring value at the back of the time sequence of the two adjacent monitoring values as 0 in the first array, and the first array is used to store the array with the difference value of 0 .

S313 , if the difference between the two adjacent monitoring values is not 0, perform difference processing on the integer part and the floating point part of the two adjacent monitoring values respectively to obtain a positive integer array by calculation.

It should be noted that, in the above steps, as shown in Table 2, Table 2 shows the monitoring value differential coding table, the collected monitoring values are arranged in time order, and all the monitoring values are traversed to determine the difference between the monitoring value and the previous point. Whether the difference value is 0, if the difference value is 0, a first array (delta array) is declared to store the index value with a difference value of 0 from the previous point.

If it is not 0, continue with the following steps:

Take the integer part X _i of the monitoring value, calculate X _i -X _i-1 and save the integer part difference array dx (default dx[0] is the integer part of the first data);

Take the fractional part of the monitoring value (four digits by default) and save it as an array ex;

为编码后结果：Traverse the integer difference array dx and the decimal integer array ex for splicing respectively. The splicing rules are as follows, where i is the ith data,

For the encoded result:

When i=0 is the first data,

；

;

Among them, rx is a positive integer array, dx is an integer part difference array, ex is a fractional part difference array, and i is the ith data.

When delta[i]=0 is established: it indicates that the monitoring value is the same as the previous monitoring value, and the record is not repeated.

；

;

Among them, rx is an array of positive integers, and i is the ith data.

When dx[i]=0 is established: it indicates that the integer part of the monitoring value is the same as the integer part of the previous monitoring value, and only the fractional part is recorded (then the length of rx[i] is 4 digits by default),

；

;

Among them, rx is a positive integer array, dx is an integer part difference array, ex is a fractional part difference array, and i is the ith data.

When dx[i]>0 is established: it indicates that the integer of the monitoring value is greater than the previous part of the monitoring value, and the splicing rule of the monitoring value is

Among them, rx is a positive integer array, dx is an integer part difference array, ex is a fractional part difference array, and i is the ith data.

>0的标志位（

长度范围在5-9位之间）;This formula means to convert the string composed of the integer difference part + the decimal difference part + '0' into an integer, and the '0' at the end is

>0 flag (

length ranges between 5-9 digits);

<0成立时：表明监测值整数大于前一个部分监测值，监测值的拼接规则为when

When <0 is established: it indicates that the integer of the monitoring value is greater than the previous part of the monitoring value, and the splicing rule of the monitoring value is

Among them, rx is a positive integer array, dx is an integer part difference array, ex is a fractional part difference array, and i is the ith data.

<0的标志位（

长度范围在5-9位之间）。This formula means to convert the string composed of the integer difference part at the front + the decimal difference part + '1' into an integer, and the '1' at the end is

<0 flag bit (

The length ranges from 5-9 digits).

After encoding, the floating-point number is converted into a positive integer between 4-9 bits and stored in 12*4=48bytes. It can be seen that converting the floating-point number into a positive integer by encoding in this way can also reduce the average length of the data and improve the data. Compression ratio, improve system resource usage efficiency.

Table 2 Monitoring value differential coding table

S320. Calculate the monitoring value encoding according to the preset encoding scale conversion model to obtain the value to be compressed, and the scale of the value to be compressed is higher than the scale of the differential encoding of the monitoring value.

In the specific embodiment disclosed in this application, step S320 includes step S321 and step S322.

S321. Perform binary conversion on the differential encoding of the monitoring value to obtain a binary code.

S322: Perform ASCII encoding on every 7 bits of the binary code to obtain a value to be compressed, and the value to be compressed is an ASCII code symbol.

It should be noted that, in the above steps, as shown in Figure 4 and Table 3, Figure 4 shows a schematic diagram of ASCII code conversion, and Table 3 shows the monitoring value ASCII encoding table, in utf-8 (Unicode Transformation Format) , single-byte characters correspond to 128 characters in ASCII code, if you can connect the information of decimal characters with ASCII code, you can convert decimal to 128, and you can store log2128 in 1 byte =7bit information, the space utilization rate reaches 87.5%. The method adopted in this application is to convert the number corresponding to the string composed of decimal characters into binary form, and then every 7 bits corresponds to an ASCII code (128 characters in the ASCII code can correspond to 0-127), so as to obtain The value to be compressed is obtained. From the result of the monitoring value ASCII encoding table, it can be seen that each record of 12 floating-point numbers needs 8 bytes to be stored before compression, and the 12 records need to be stored in 12*8=96bytes in total. After encoding, the floating-point number is converted into 4- Positive integers between 9 digits are stored in 12*4=48bytes, and then the integers are converted into ASCII codes after bitwise AND operation based on the base compression conversion encoding method, and the storage cost is 3+2+2+2+1+1+ 2+3+2+2+2+2=24bytes has a compression ratio of 4 compared to the 96bytes that initially stores floating-point numbers, which effectively reduces storage costs.

Table 3 Monitoring value ASCII code table

In the specific embodiment disclosed in this application, step S400 includes step S410, step S420 and step S430.

S410. Calculate and obtain a character frequency table according to the numerical value to be compressed, where the character frequency table includes the numerical value in the numerical value to be compressed and the frequency corresponding to the numerical value.

S420. Perform Huffman encoding calculation on the character frequency table to obtain a Huffman encoding table.

S430. Calculate and obtain the compression result of the monitoring value according to the value to be compressed and the Huffman coding table.

It should be noted that, in the above steps, the present application proposes to convert decimal integers into ASCII codes of 128 bases on the basis of system conversion. The purpose of this is to better combine the characteristics of Huffman coding. , the coding range only includes 128 ASCII codes, the application uses the Huffman principle to make assumptions about the occurrence frequency of ASCII code symbols in the above-mentioned 8 monitoring data to construct a Huffman coding tree. The symbol frequency is obtained based on ASCII shown in Table 4. The code frequency corresponds to the Huffman coding table. According to the frequency of occurrence of ASCII codes calculated in Table 4, it takes a total of 10000 bytes of space to store ASCII codes, and the size is 200+300+600+700+1000+3200+1900+2100=10000 bytes. A total of 3275bytes is required for table storage. The size is 200*5+300*5+600*4+700*4+1000*4+3200*2+1900*2+2100*2)/8bit=3274.5bytes+8bytes+4bytes ( The last 8+4bytes is the storage cost of the Huffman coding table), and its compression ratio of 3.05 indicates that the compression effect is good, the compression coding of the monitoring value is completed, the storage pressure of the database is reduced, and the resource utilization rate of the system is improved.

Table 4 Corresponding Huffman coding table based on ASCII code frequency

Example 2:

As shown in FIG. 2 , this embodiment provides an apparatus for lossless compression of monitoring data. The apparatus includes:

The data acquisition module 1 is used for acquiring a data table to be compressed, and the data table to be compressed includes monitoring values collected by sensors and time data corresponding to each monitoring value.

The first calculation module 2 is used to calculate all time data according to a preset time difference mathematical model to obtain a time stamp compression result, and the time stamp compression result includes a starting time stamp and all time data after the secondary difference method is processed. The resulting timestamp quadratic difference.

The second calculation module 3 is configured to calculate all the monitored values according to a preset mathematical model of conversion of data to system to obtain the numerical value to be compressed.

The third calculation module 4 is configured to calculate the value to be compressed according to the preset Huffman coding mathematical model to obtain the compression result of the monitoring value.

In some specific embodiments, the first computing module 2 includes:

The first calculation unit 21 is used for converting the time data into the UNIX timestamp format and arranging the timestamp data columns according to the time sequence.

The second calculation unit 22 is configured to calculate the timestamp data column according to the preset timestamp quadratic difference method mathematical model to obtain the start timestamp and the timestamp quadratic difference.

In some specific embodiments, the second computing module 3 includes:

The third calculation unit 31 is configured to calculate all monitoring values according to a preset differential coding mathematical model to obtain monitoring value codes, and the monitoring value codes are decimal codes.

The fourth calculation unit 32 is configured to calculate the monitoring value encoding according to the preset encoding scale conversion model to obtain the to-be-compressed value, and the scale of the to-be-compressed value is higher than that of the differential encoding of the monitoring value.

In some specific embodiments, the third computing unit 31 includes:

The first judging unit 311 is configured to traverse all monitoring values, perform difference calculation on two adjacent monitoring values according to time sequence, and judge whether the calculation result is 0.

The second judging unit 312 is configured to judge that if the difference between the adjacent monitoring values is 0, store the monitoring value data that is later in time sequence among the two adjacent monitoring values as 0 in the first array, and the first array is used for Stores an array with a difference of 0.

The third judging unit 313 is used for judging that if the difference between two adjacent monitoring values is not 0, the integer part and the floating point part of the two adjacent monitoring values are respectively subjected to difference processing, and a positive integer array is obtained by calculation.

In some specific embodiments, the fourth computing unit 32 includes:

The fifth calculation unit 321 is configured to perform binary conversion on the differential code of the monitoring value to obtain a binary code.

The sixth calculation unit 322 is configured to perform ASCII encoding on every 7 bits of the binary code to obtain a numerical value to be compressed, and the numerical value to be compressed is an ASCII code symbol.

In some specific embodiments, the third computing module 4 includes:

The seventh calculation unit 41 is configured to calculate and obtain a character frequency table according to the numerical value to be compressed, where the character frequency table includes the numerical value in the numerical value to be compressed and the frequency corresponding to the numerical value.

The eighth calculation unit 42 is configured to perform Huffman coding calculation on the character frequency table to obtain a Huffman coding table.

The ninth calculation unit 43 is configured to calculate and obtain the compression result of the monitoring value according to the value to be compressed and the Huffman coding table.

Example 3:

Corresponding to the above method embodiments, this embodiment also provides a device for lossless compression of monitoring data, and a device for lossless compression of monitoring data described below and a method for lossless compression of monitoring data described above may refer to each other correspondingly.

FIG. 3 is a block diagram of a lossless compression device 800 for monitoring data according to an exemplary embodiment. As shown in FIG. 3 , the monitoring data lossless compression device 800 may include: a processor 801 and a memory 802 . The monitoring data lossless compression device 800 may also include one or more of a multimedia component 803 , an I/O interface 804 , and a communication component 805 .

The processor 801 is configured to control the overall operation of the monitoring data lossless compression device 800, so as to complete all or part of the steps in the above-mentioned monitoring data lossless compression method. The memory 802 is used to store various types of data to support operation of the monitoring data lossless compression device 800, which data may include, for example, instructions for any application or method operating on the monitoring data lossless compression device 800, and Application-related data, such as contact data, messages sent and received, pictures, audio, video, and more. The memory 802 can be implemented by any type of volatile or non-volatile memory device or a combination thereof, such as Static Random Access Memory (SRAM for short), Electrically Erasable Programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory) Erasable Programmable Read-Only Memory, EEPROM for short), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read Only Memory (Read -Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. Multimedia components 803 may include screen and audio components. Wherein the screen can be, for example, a touch screen, and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in memory 802 or transmitted through communication component 805 . The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules may be a keyboard, a mouse, a button, and the like. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the monitoring data lossless compression device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC for short), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 may include: Wi-Fi -Fi module, bluetooth module, NFC module.

In an exemplary embodiment, the monitoring data lossless compression device 800 may be composed of one or more application specific integrated circuits (Application Specific Integrated Circuit, ASIC for short), digital signal processor (Digital Signal Processor, DSP for short), digital signal processing equipment (Digital Signal Processing Device, referred to as DSPD), Programmable Logic Device (Programmable Logic Device, referred to as PLD), Field Programmable Gate Array (Field Programmable Gate Array, referred to as FPGA), controller, microcontroller, microprocessor or other electronic The component implementation is used to perform the above-mentioned lossless compression method of monitoring data.

In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned method for lossless compression of monitoring data are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions can be executed by the processor 801 of the monitoring data lossless compression device 800 to complete the above-mentioned monitoring data lossless compression method.

Example 4:

Corresponding to the above method embodiments, a readable storage medium is also provided in this embodiment, and a readable storage medium described below and a lossless compression method for monitoring data described above can be referred to each other correspondingly.

A readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, implements the steps of the monitoring data lossless compression method of the above method embodiment.

The readable storage medium may specifically be a USB flash drive, a mobile hard disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, etc. that can store program codes. Readable storage medium.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention. should be included within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims (8)

1. a monitoring data lossless compression method, is characterized in that, comprises: acquiring a data table to be compressed, the data table to be compressed includes monitoring values collected by a sensor and time data corresponding to each monitoring value, and each monitoring value is collected by the sensor in chronological order; Calculate all the time data according to the preset time difference mathematical model to obtain a time stamp compression result, and the time stamp compression result includes a starting time stamp and all the time data obtained by processing the second difference method Timestamp secondary difference; Calculate all the monitoring values according to the preset data conversion mathematical model to obtain the values to be compressed; Calculate the to-be-compressed value according to a preset Huffman coding mathematical model to obtain a monitoring value compression result; All the monitored values are calculated according to the preset mathematical model of digital conversion to obtain the values to be compressed, including: According to the preset differential coding mathematical model, all the monitoring values are calculated to obtain the monitoring value codes, and the monitoring value codes are decimal codes; According to the preset code conversion model, the monitoring value encoding is calculated to obtain the to-be-compressed value, and the system of the to-be-compressed value is higher than the system of the differential encoding of the monitoring value; Described according to the preset differential coding mathematical model, all the monitoring values are calculated to obtain the monitoring value codes, and the monitoring value codes are decimal codes, including: Traverse all monitoring values, calculate the difference between two adjacent monitoring values according to the time sequence, and judge whether the calculation result is 0; If the difference between the adjacent monitoring values is 0, the next monitoring value data in the time sequence of the two adjacent monitoring values is stored as 0 in the first array, and the first array is used to store the array with the difference value of 0; If the difference between the two adjacent monitoring values is not 0, the integer part and the floating point part of the two adjacent monitoring values are respectively subjected to difference processing to obtain an array of positive integers. 2. The method for lossless compression of monitoring data according to claim 1, wherein the time stamp compression result is obtained by calculating all the time data according to a preset time difference mathematical model, comprising: Converting the time data into a UNIX timestamp form and arranging in chronological order to obtain a timestamp data column; Calculate the timestamp data column according to the preset timestamp quadratic difference method mathematical model to obtain the start timestamp and the timestamp quadratic difference. 3. the monitoring data lossless compression method according to claim 1, is characterized in that, according to preset Huffman coding mathematical model, described numerical value to be compressed is calculated to obtain monitoring value compression result, comprising: Calculate and obtain a character frequency table according to the to-be-compressed numerical value, and the character frequency table includes the numerical value in the to-be-compressed numerical value and the frequency corresponding to the numerical value; Carry out Huffman coding calculation to the character frequency table to obtain the Huffman coding table; The monitoring value compression result is obtained by calculating according to the to-be-compressed value and the Huffman coding table. 4. A lossless compression device for monitoring data, comprising: a data acquisition module, configured to obtain a data table to be compressed, where the data table to be compressed includes monitoring values collected by sensors and time data corresponding to each of the monitoring values; The first calculation module is used to calculate all the time data according to the preset time difference mathematical model to obtain a time stamp compression result, and the time stamp compression result includes a start time stamp and all the time data for the second time. The secondary difference of timestamps obtained after processing by the difference method; a second calculation module, configured to calculate all the monitoring values according to a preset mathematical model of conversion of data to system to obtain numerical values to be compressed; a third calculation module, configured to calculate the to-be-compressed value according to a preset Huffman coding mathematical model to obtain a monitoring value compression result; The second computing module includes: a third calculation unit, configured to calculate all the monitoring values according to a preset differential coding mathematical model to obtain a monitoring value code, and the monitoring value code is a decimal code; a fourth calculation unit, configured to calculate the monitoring value code according to a preset code conversion model to obtain a numerical value to be compressed, and the base of the numerical value to be compressed is higher than the base value of the differential encoding of the monitoring value; The third computing unit includes: a first judgment unit, used for traversing all monitoring values, performing difference calculation on two adjacent monitoring values according to time sequence, and judging whether the calculation result is 0; The second judgment unit is used for judging that if the difference between the adjacent monitoring values is 0, then the monitoring value data of the next two adjacent monitoring values in time sequence is stored as 0 in the first array, and the first array is used for storing An array with a difference of 0; The third judging unit is used for judging that if the difference between the two adjacent monitoring values is not 0, the integer part and the floating point part of the two adjacent monitoring values are respectively subjected to difference processing, and a positive integer array is obtained by calculation. 5. The monitoring data lossless compression device according to claim 4, wherein the first calculation module comprises: The first computing unit is used to convert the time data into a UNIX timestamp form and arrange the timestamp data columns in chronological order; The second calculation unit is configured to calculate the timestamp data column according to the preset timestamp quadratic difference method mathematical model to obtain the start timestamp and the timestamp quadratic difference. 6. The monitoring data lossless compression device according to claim 4, wherein the third calculation module comprises: A seventh calculation unit, configured to calculate and obtain a character frequency table according to the numerical value to be compressed, and the character frequency table includes the frequency corresponding to the numerical value in the numerical value to be compressed and the numerical value; The eighth calculation unit, for carrying out the Huffman coding calculation to the character frequency table to obtain the Huffman coding table; A ninth calculation unit, configured to calculate and obtain a monitoring value compression result according to the to-be-compressed value and the Huffman coding table. 7. A lossless compression device for monitoring data, comprising: a memory for storing a computer program; The processor is configured to implement the steps of the method for lossless compression of monitoring data according to any one of claims 1 to 3 when executing the computer program. 8. A readable storage medium, characterized in that: a computer program is stored on the readable storage medium, and when the computer program is executed by a processor, lossless compression of monitoring data according to any one of claims 1 to 3 is realized steps of the method.

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