Monitoring data remote acquisition method and system based on free ion generator

Abstract

The invention relates to the field of data processing, in particular to a monitoring data remote acquisition method and system based on a free ion generator, and the method comprises the steps: obtaining ion data of an ion flow field region at different monitoring moments and a plurality of air information type concentration data; dividing the ion flow field area into a plurality of ion flow field position point areas; according to the air information type concentration data, a directivity expression coefficient is obtained, and then diffusion weakening expression is obtained; according to diffusion weakening performance, analyzing consistency change conditions of diffusion weakening conditions of different ion flow field position point areas in the local area to obtain diffusion sensitivity coefficients of the different ion flow field position point areas; and performing remote intelligent data acquisition according to the diffusion sensitivity coefficient. According to the invention, through monitoring data remote uploading frequency adjustment, the accuracy of monitoring environment dynamic adjustment intensity is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing, and more specifically to a method and system for remotely acquiring monitoring data based on a free ion generator. Background Technology

[0002] A free ion generator is a device that can ionize oxygen atoms and water molecules in the air at a relatively low operating voltage. The plasma density in the generator's electric field region can reach 10¹⁶ per cubic meter, with an average electron energy of 0.31 eV. High-density particle swarms, including charged and metastable particles, along with electrons driven by Brownian motion and wind, can create a plasma flow field in a space of approximately 200 square meters, directly killing airborne viruses and rapidly blocking their transmission routes. APJD technology allows for coexistence between humans and the generator, preventing cross-infection among people.

[0003] However, in actual use cases of free ion generators, factors such as people walking in the generator's electric field area can affect the local airflow speed, increasing the uneven diffusion of the particle swarm generated by the ion generator. This leads to a significant attenuation effect of the generated plasma flow field, affecting the disinfection effect of the generator's electric field area. Ultimately, this results in poor performance of the dynamic adjustment of free ion generation intensity based on pollutant concentration feedback achieved through remote acquisition of environmental monitoring data from multiple types of sensors. Summary of the Invention

[0004] This invention provides a method and system for remotely acquiring monitoring data from a free ion generator to solve existing problems: the disinfection effect of a free ion generator depends on the ion concentration in the plasma flow field. When there are factors in the environment, such as personnel movement, that affect the diffusion process of free ions, the remote acquisition frequency of multiple types of sensors in the fixed free ion generator is reduced, and the extracted environmental monitoring data cannot accurately capture the dynamic changes of free ions. As a result, it is difficult to achieve adaptive adjustment of the power of the free ion generator to effectively remove harmful substances.

[0005] The present invention relates to a method and system for remote acquisition of monitoring data from a free ion generator, which adopts the following technical solution: This invention proposes a method for remotely acquiring monitoring data from a free ion generator, which includes the following steps: Acquire ion data and concentration data of several air information types in the ion flow field region at different monitoring times; The ion flow field region is divided into several ion flow field location point regions. Based on the air information type concentration data, and the influence of personnel flow on the monitoring data of multiple monitoring locations in the ion flow field, the similarity of ion diffusion direction in different ion flow field location point regions is analyzed to obtain the directional performance coefficient of each ion flow field location point region at each monitoring time. Based on the directional performance coefficient and ion data, the diffusion process of free ions at different monitoring times is gradually slowed down, and the diffusion weakening performance of each ion flow field location at each monitoring time is obtained. Based on the diffusion attenuation behavior, the consistency of diffusion attenuation in local regions at different ion flow field locations is analyzed to obtain the diffusion sensitivity coefficients at different ion flow field locations. Based on the diffusion sensitivity coefficient, the frequency of air information type concentration data being uploaded to the remote processing center is adjusted, and then uploaded to the cloud processing center for processing, thereby realizing remote intelligent acquisition of monitoring data based on the free ion generator.

[0006] Preferably, the method for obtaining the directional performance coefficient is as follows: Obtain the ion diffusion direction of all ion flow field location points at the same monitoring time; At the same monitoring time, the angle between the ion diffusion direction and the horizontal line of each ion flow field location region is taken as the directional degree; the difference in directional degree between different ion diffusion directions is taken as the distance metric. All ion diffusion directions are clustered according to the distance metric to obtain multiple clusters; any ion flow field location region within any cluster is taken as the labeled ion flow field location region; each other ion flow field location region within the eight neighborhoods of the labeled ion flow field location region is taken as the neighboring connected region of the labeled ion flow field location region; the absolute value of the difference in directional degree between the labeled ion flow field location region and each neighboring connected region is taken as the directional similarity of each neighboring connected region. All neighboring connected regions are sorted in ascending order according to directional similarity to obtain the sequence of neighboring location regions of the labeled ion flow field location region. In the sequence of neighboring location points of the marked ion flow field location point region, the average difference of the cumulative difference in directional similarity between two adjacent neighboring connected regions is used as the directional performance coefficient of the marked ion flow field location point region; the directional performance coefficient of each ion flow field location point region at the same monitoring time is obtained.

[0007] Preferably, the method for obtaining the ion diffusion direction is as follows: Obtain the air pollutant type vector for each ion flow field location area at the same monitoring time; take any ion flow field location area as the target location area, and take the target location area as the center, obtain the difference vector between the air pollutant type vector of the target location area and each other ion flow field location area in the surrounding eight neighborhoods; obtain the determinant value of the difference vector of each air pollutant type vector; take the direction pointed to by the difference vector with the largest determinant value as the ion diffusion direction of the target location area; obtain the ion diffusion direction of each ion flow field location area at the same monitoring time.

[0008] Preferably, the method for obtaining the air pollutant type vector is as follows: In any ion flow field location area, the vector composed of concentration data of different types of air information at the same monitoring time is used as the air pollutant type vector of that ion flow field location area at the same monitoring time.

[0009] Preferably, the method for obtaining the diffusion attenuation performance is as follows: In any ion flow field location region, any monitoring time is taken as the target monitoring time. The difference in directional performance coefficient between the target monitoring time and the previous monitoring time is calculated as the directional performance difference value of the target monitoring time. The ratio of the directional performance difference value to the directional performance coefficient of the target monitoring time is used as the monitoring time as the diffusion weakening performance of the ion flow field location region at the target time.

[0010] Preferably, the method for obtaining the diffusion sensitivity coefficient is as follows: Using any ion flow field location region as a reference location region and any monitoring time as a reference monitoring time, calculate the average difference in diffusion attenuation between the reference location region and other ion flow field location regions at the target monitoring time; determine the diffusion sensitivity coefficient of the ion flow field location region based on the temporal cumulative performance of the average difference value of the reference location region.

[0011] The present invention also proposes a remote acquisition system for monitoring data based on a free ion generator, including a memory and a processor. The processor executes a computer program stored in the memory to implement the steps of the above-described remote acquisition method for monitoring data based on a free ion generator.

[0012] The beneficial effects of the technical solution of this invention are as follows: This invention divides the ion flow field region into several ion flow field location point regions; wherein the ion flow field location point regions represent areas for measuring the local ion diffusion of the ion flow field region, it can achieve regional representation of the ion diffusion status of the ion flow field; then, based on the air information type concentration data, and based on the influence of personnel flow on the monitoring data at multiple monitoring locations of the ion flow field, it analyzes the similarity of ion diffusion directions in different ion flow field location point regions, and obtains the directional performance coefficient; this makes the influence of personnel flow on the ion diffusion process in the ion flow field location point regions more obvious; then, based on the directional performance coefficient and ion data, it analyzes the ion diffusion in the ion flow field location point regions at different monitoring times. The gradual slowing down of the free ion diffusion process results in diffusion attenuation. This makes the ion diffusion direction, influenced by personnel movement at different monitoring times, more similar, thus improving the distinguishability of the attenuation effect. Finally, based on the diffusion attenuation effect, the consistency of diffusion attenuation in local areas of different ion flow field locations is analyzed, yielding diffusion sensitivity coefficients for different ion flow field locations. This further amplifies the errors caused by the fixed remote upload frequency of monitoring data at different ion flow field locations. This invention improves the accuracy of dynamic adjustment of the monitoring environment by analyzing the ion diffusion direction and attenuation process at ion flow field locations and adjusting the remote upload frequency of monitoring data. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart of the steps of the remote acquisition method for monitoring data based on a free ion generator according to the present invention. Detailed Implementation

[0015] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the remote acquisition method and system for monitoring data based on a free ion generator proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0016] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0017] The specific solution of the remote acquisition method and system for monitoring data based on free ion generator provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0018] Please see Figure 1 The diagram illustrates a flowchart of a method for remotely acquiring monitoring data from a free ion generator according to an embodiment of the present invention. The method includes the following steps: Step S001: Obtain ion data and concentration data of several air information types in the ion flow field region at different monitoring times.

[0019] It should be noted that the diffusion process of free ions generated by the free ion generator within the space to be disinfected is primarily driven by Brownian motion and wind. The device itself uses a micro-turbo fan to disturb the airflow and diffuse the ion flow field. When people are moving within the space, their activities alter the local airflow speed, increasing the unevenness of ion diffusion. Some areas will experience a replenishment of ion concentration due to the free ion diffusion process, effectively killing viruses in the air, while other areas will not reach the required ion concentration for disinfection. Furthermore, the data extraction frequency of the various types of sensors in the fixed free ion generator makes it difficult to accurately capture the dynamic changes of free ions, thus hindering the adaptive adjustment of the free ion generator's power to effectively remove harmful substances.

[0020] Specifically, an arbitrarily selected area is designated as the ion flow field region. A free ion generator is positioned at the center of this region, and various types of air quality monitoring sensors are arranged at equal intervals. Every second is considered a monitoring moment, recording the air quality type concentration data from each monitoring sensor and the ion data from the free ion generator for a total of one hour. Air quality type concentration data and ion data are then acquired at several monitoring moments. The initial data acquisition and upload frequency is defined as recording the air quality type concentration data from each monitoring sensor and the ion data from the free ion generator every second.

[0021] It should be noted that the types of air information monitoring sensors in this invention are described using three types of air information monitoring sensors that detect PM2.5, ammonia, and temperature and humidity as examples. Five of each type of air information monitoring sensor are arranged in this invention. The types and number of air information monitoring sensors can be determined according to the specific implementation situation, and will not be described in detail in this embodiment.

[0022] Thus, the above methods were used to obtain ion data and concentration data of several air information types in the ion flow field region at different monitoring times.

[0023] Step S002: Divide the ion flow field region into several ion flow field location point regions; based on the air information type concentration data, and the influence of personnel flow on the monitoring data of multiple monitoring locations in the ion flow field, analyze the similarity of ion diffusion direction in different ion flow field location point regions, and obtain the directional performance coefficient of each ion flow field location point region at each monitoring time.

[0024] It should be noted that the diffusion process of free ions is mainly affected by three factors: Brownian motion, wind-driven diffusion, and human movement. Brownian motion diffusion is a random process with similar effects at all locations. Wind-driven diffusion is mainly affected by the airflow of the micro-turbo fan of the free ion generator. Human movement is caused by the interaction between the gas in the local area near the human body and human activity, which changes the local airflow speed and thus affects the direction and rate of ion diffusion. Therefore, the influence of human movement on the diffusion process is limited, and it manifests as certain local anomalies in the entire ion flow field and the direction of human movement.

[0025] Specifically, a preset number of regions is used. The entire ion flow field region is divided longitudinally into equal parts. Each region is designated as a location point region for the ion flow field. In this embodiment, [the following is used as an example]. This example illustrates the concept, and implementers can adjust the settings according to their specific circumstances.

[0026] Furthermore, taking any ion flow field location point region as an example, within this ion flow field location point region, the vector composed of different types of air information concentration data at the same monitoring time is used as the air pollutant type vector of the ion flow field location point region at the same monitoring time; and the air pollutant type vectors of all ion flow field location point regions at the same monitoring time are obtained.

[0027] It should be further explained that, due to the diffusion effect of free ions in the local area caused by the movement of people, the diffusion of free ions changes when people arrive at the location. The greater the change in the direction of the local airflow velocity, and the more continuous the local airflow velocity changes at the location point in the direction of the movement of people, the more obvious the directional manifestation of free ion diffusion at that location point is affected by the movement of people.

[0028] Specifically, taking any ion flow field location point region as an example, this ion flow field location point region is regarded as the center, and the difference vector of air pollutant type vector between this ion flow field location point region and each other ion flow field location point region in the surrounding eight neighboring regions is obtained; the determinant value of each air pollutant type vector difference vector is obtained; the direction pointed to by the difference vector with the largest determinant value is taken as the ion diffusion direction of this ion flow field location point region; and the ion diffusion direction of all ion flow field location point regions at the same monitoring time is obtained.

[0029] It should be further explained that, due to the diffusion of free ions in a local area caused by the movement of people, the direction of ion diffusion at a location point, that is, the direction of ion diffusion under the influence of local airflow speed, is mainly driven by wind in the ion flow field. The change in the direction of ion diffusion caused by the movement of people is more of a consistent direction in the local neighborhood.

[0030] Specifically, at the same monitoring time, the angle between the ion diffusion direction and the horizontal line in each ion flow field location area is used as the directional degree; the difference in directional degree between different ion diffusion directions is used as the distance metric. All ion diffusion directions are clustered based on the distance metric to obtain multiple clusters. This embodiment uses the k-means clustering algorithm as an example for description. This example is used for illustration, but the specific settings can be adjusted according to the actual situation. In addition, the k-means clustering algorithm is a well-known technology, and will not be described in detail in this embodiment.

[0031] Furthermore, taking any ion flow field location region within any cluster as an example, each ion flow field location region within the eight neighborhoods of this ion flow field location region is taken as the neighboring connected region of this ion flow field location region; the absolute value of the difference in directional degree between this ion flow field location region and each neighboring connected region is taken as the directional similarity of each neighboring connected region, and all neighboring connected regions are arranged in ascending order according to directional similarity to obtain the sequence of neighboring location regions of this ion flow field location region.

[0032] Furthermore, as an example, the directional performance coefficient can be calculated using the following formula: in, This indicates the number of elements within the cluster. The region of the ion flow field location point. The directional performance coefficient at each monitoring moment This indicates the number of elements within the cluster. The region of the ion flow field location point. Air pollutant type vector at each monitoring time, This indicates the number of elements within the cluster. The region of the ion flow field location point. Within the sequence of neighborhood location points of the ion flow field location point region at the monitoring time, the [number]th location point... A vector of air pollutant types in a region of ion flow field location points. This represents the determinant of the difference vector of air pollutant type vectors between two ion flow field location points within the cluster. This indicates the number of elements within the cluster. The region of the ion flow field location point. The direction of ion diffusion at each monitoring moment. This indicates the number of elements within the cluster. The region of the ion flow field location point. Within the sequence of neighborhood location points of the ion flow field location point region at the monitoring time, the [number]th location point... The direction of ion diffusion in the region of each ion flow field location. This represents the difference in ion diffusion direction between two ion flow field locations within the cluster. This indicates the number of elements within the cluster. The region of the ion flow field location point. The number of all ion flow field location point regions within the neighborhood location point region sequence of the ion flow field location point region at each monitoring time. Indicates taking the absolute value; This represents the normalization function.

[0033] It should be noted that if the first The region of the ion flow field location point. The larger the cumulative value of the determinant of the difference vector at each monitoring moment, the greater the influence of personnel movement on the diffusion of free ions in the local area of ​​the ion flow field, and the more obvious the directional behavior of free ion diffusion.

[0034] Thus, the directional performance coefficients of each monitoring moment in the region of each ion flow field location are obtained using the above method.

[0035] Step S003: Based on the directional performance coefficient and ion data, analyze the gradual slowdown of the free ion diffusion process at the ion flow field location point region under different monitoring times, and obtain the diffusion weakening performance of each ion flow field location point region at each monitoring time.

[0036] It should be noted that, when free ions in an ion flow field are only affected by Brownian motion, their randomness leads to uniform diffusion. However, actual personnel movement disrupts this ideal state. That is, when the free ions in the ion flow field location area are greatly affected by personnel movement at the corresponding monitoring time, their diffusion will intensify. When personnel leave the ion flow field location area, their diffusion will be affected by Brownian motion and gradually tend towards a stable state. Therefore, the diffusion of free ions at multiple locations is a gradual weakening. The more consistent the weakening is across all locations, the more significant the Brownian motion is at that ion flow field location area. In other words, the fixed-frequency remote data acquisition method at that ion flow field location area has less impact on the judgment of the ion flow field condition.

[0037] Specifically, as an example, the diffusion attenuation performance can be calculated using the following formula: in, Indicates the first The region of the ion flow field location point. The diffusion weakening effect at each monitoring moment Indicates the first The region of the ion flow field location point. The directional performance coefficient at each monitoring moment Indicates the first The region of the ion flow field location point. The directional performance coefficient at each monitoring moment Indicates the first The region of the ion flow field location point. The monitoring time and the first The difference in directional performance coefficient at each monitoring moment Indicates the first The higher the index of a monitoring moment within all monitoring moments, the greater the probability of being affected by the weakening effect of ion diffusion. Indicates the first The region of the ion flow field location point. The rate of change of the directional performance at each monitoring moment is the rate of change at that sampling moment. The smaller the value, the more obvious the weakening effect. Indicates taking the absolute value; This represents a preset hyperparameter used to prevent the denominator from being 0. In this embodiment, it is used as... Let's take an example to illustrate; This represents an exponential function with the natural constant as its base. The example uses... The model is used to represent the inverse proportional relationship and for normalization processing. As input to the model, implementers can choose between an inverse proportional function and a normalization function based on the actual situation.

[0038] Thus, the diffusion attenuation performance of each ion flow field location region at each monitoring time was obtained using the above method.

[0039] Step S004: Based on the diffusion attenuation performance, analyze the consistency of diffusion attenuation in local areas at different ion flow field locations to obtain the diffusion sensitivity coefficients at different ion flow field locations.

[0040] It should be noted that, due to the fixed-frequency remote data acquisition method, the state of the ion flow field is mainly reflected in the weakening of diffusion at the location point of the ion flow field. As the ion diffusion changes, it will gradually become consistent with the weakening of diffusion at the location point of the surrounding ion flow field. Since the proportion of data affected by personnel movement is relatively small over a long period of time, the ion diffusion status at all location point areas will show a weakening trend. Therefore, any significant anomalies in the weakening trend at a single location point area are more likely due to errors in the remote data acquisition method.

[0041] As an example, the diffusion sensitivity coefficient can be calculated using the following formula: in, Indicates the first The diffusion sensitivity coefficient of a region at a given ion flow field location. Indicates the first The region of the ion flow field location point. The diffusion weakening effect at each monitoring moment Indicates except the first The first ion flow field location point region outside the region of all other ion flow field location points. The mean of the diffusion attenuation performance at each monitoring moment. This indicates the number of all monitored moments.

[0042] Thus, the diffusion sensitivity coefficient of each ion flow field location region is obtained using the above method.

[0043] Step S005: Adjust the frequency of monitoring data transmission to the remote center based on the diffusion sensitivity coefficient, and then upload the data to the cloud processing center for processing to achieve remote intelligent acquisition of monitoring data based on the free ion generator.

[0044] It should be noted that, based on the diffusion sensitivity coefficient of the obtained ion flow field location area, the data acquisition and upload frequency of the sensor equipment in that ion flow field location area can be adjusted, thereby adjusting the power of the free ion generator.

[0045] Specifically, taking any ion flow field location as an example, a diffusion sensitivity coefficient threshold is preset. When the diffusion sensitivity coefficient of the ion flow field location region is greater than At that time, the region of the ion flow field location is marked as an abnormal node in remote monitoring and acquisition; all abnormal nodes in remote monitoring and acquisition are acquired. This embodiment uses... This example is used for illustration; no specific limitations are set in this embodiment. It depends on the specific implementation situation.

[0046] Furthermore, a pre-defined location influence range is established. Taking each remotely monitored abnormal node as the center, with a radius of... The affected area is defined as the region where each remote monitoring node experiences an anomaly. Taking any air quality monitoring sensor as an example, if the sensor exists within the affected area of ​​some remote monitoring nodes experiencing anomalies, the data acquisition and upload frequency of that sensor is adjusted. As an example, the new data acquisition and upload frequency of the air quality monitoring sensor is calculated using the following formula: in, This indicates the new data acquisition and upload frequency of the air information monitoring sensor; This indicates the initial data acquisition and upload frequency of the air information monitoring sensor; This indicates the number of all remote monitoring and data acquisition anomaly nodes within the affected area that contain the air information monitoring sensor; This indicates that among all remote monitoring and data acquisition anomaly nodes containing the air information monitoring sensor within the affected area, the [number]th [node] is [the one with the abnormal data]. The diffusion sensitivity coefficient of an abnormal node collected by remote monitoring; This represents the mean of the diffusion sensitivity coefficients of all remotely monitored and collected abnormal nodes.

[0047] Furthermore, if the air information monitoring sensor is not present in the affected area of ​​all remote monitoring and data acquisition anomaly nodes, then there is no need to adjust the data acquisition and upload frequency of the air information monitoring sensor.

[0048] It should be noted that, in this embodiment, the following is used: This example is used for illustration; no specific limitations are set in this embodiment. It depends on the specific implementation situation.

[0049] The above steps complete the method for remotely acquiring monitoring data based on a free ion generator.

[0050] Another embodiment of the present invention provides a remote acquisition system for monitoring data based on a free ion generator. The system includes a memory and a processor. When the processor executes a computer program stored in the memory, it performs the above-described method steps S001 to S005.

[0051] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for remote acquisition based on monitoring data from a free ion generator, characterized in that, The method comprises the following steps: Obtain ion data and concentration data of air information types of the ion flow field region at different monitoring time points; Divide the ion flow field region into a plurality of ion flow field position point regions; based on the concentration data of the air information types, analyze the similarity of ion diffusion directions of different ion flow field position point regions based on the monitoring data of the ion flow field at a plurality of monitoring positions affected by personnel flow, and obtain a direction performance coefficient of each monitoring time point of each ion flow field position point region; Based on the direction performance coefficient and the ion data, analyze the gradual slowing down of the free ion diffusion process in the ion flow field position point region at different monitoring time points, and obtain the diffusion weakening performance of each ion flow field position point region at each monitoring time point; Based on the diffusion weakening performance, analyze the consistency of the diffusion weakening status of different ion flow field position point regions in the local region, and obtain the diffusion sensitivity coefficient of different ion flow field position point regions; Based on the diffusion sensitivity coefficient, adjust the remote upload frequency state of the concentration data of the air information types, and upload to the cloud processing center for processing, to realize remote intelligent acquisition based on the monitoring data of the free ion generator.

2. The method of claim 1, wherein the method further comprises: The method for obtaining the direction performance coefficient comprises the following steps: Obtain the ion diffusion direction of all ion flow field position point regions at the same monitoring time point; At the same monitoring time point, take the angle between the ion diffusion direction of each ion flow field position point region and the horizontal line as the direction degree; take the difference in direction degree between different ion diffusion directions as the distance measure, cluster all ion diffusion directions according to the distance measure, and obtain a plurality of clustering clusters; take any ion flow field position point region in any clustering cluster as a marked ion flow field position point region; Take each ion flow field position point region in the eight-neighborhood of the ion flow field position point region as a neighborhood connection region of the marked ion flow field position point region; Take the absolute value of the difference in direction degree between the marked ion flow field position point region and each neighborhood connection region as the direction similarity of each neighborhood connection region, and arrange all neighborhood connection regions in ascending order according to the direction similarity to obtain a neighborhood position point region sequence of the marked ion flow field position point region; In the neighborhood position point region sequence of the marked ion flow field position point region, take the overall difference between the direction similarity of the adjacent two neighborhood connection regions as the direction performance coefficient of the marked ion flow field position point region; and obtain the direction performance coefficient of each ion flow field position point region at the same monitoring time point.

3. The method of claim 2, wherein the method further comprises: The method for obtaining the ion diffusion direction comprises the following steps: An air pollutant type vector of each ion flow field position point area at the same monitoring moment is obtained; any one ion flow field position point area is taken as a target position point area, and the target position point area is taken as a center to obtain an air pollutant type vector difference vector between the target position point area and each ion flow field position point area in the surrounding eight neighborhoods; a determinant value of each air pollutant type vector difference vector is obtained; a direction indicated by the difference vector with the largest determinant value is taken as an ion diffusion direction of the target position point area; and the ion diffusion direction of each ion flow field position point area at the same monitoring moment is obtained.

4. The method of claim 3, wherein the data is obtained from a free ion generator. The air pollutant type vector is obtained by the following method: In any one ion flow field position point area, a vector formed by different types of air information type concentration data at the same monitoring moment is taken as an air pollutant type vector of the ion flow field position point area at the same monitoring moment.

5. The method of claim 1, wherein the method further comprises: The diffusion weakening performance is obtained by the following method: In any one ion flow field position point area, any one monitoring moment is taken as a target monitoring moment, a difference value of directional performance coefficients between the target monitoring moment and a previous monitoring moment is calculated as a directional performance difference value of the target monitoring moment; and a ratio of the directional performance difference value to the directional performance coefficient of the target monitoring moment is taken as a diffusion weakening performance of the ion flow field position point area at the target moment.

6. The method of claim 1, wherein the method further comprises: The diffusion sensitivity coefficient is obtained by the following method: Any one ion flow field position point area is taken as a reference position point area, and any one monitoring moment is taken as a reference monitoring moment; an average difference value of diffusion weakening performances between the reference position point area and other ion flow field position point areas at the target monitoring moment is calculated; A diffusion sensitivity coefficient of the ion flow field position point area is determined according to a time sequence cumulative performance of the average difference value of the reference position point area.

7. A remote acquisition system based on free ion generator monitoring data, comprising a memory, a processor and a computer program stored in said memory and running on said processor, characterized in that, The processor implements the steps of the remote acquisition method based on the monitoring data of the free ion generator according to any one of claims 1-6 when executing the computer program.

8. Remote acquisition device based on free ion generator monitoring data, characterized in that, The device comprises: a data acquisition module, a diffusion sensitivity coefficient acquisition module and a remote intelligent acquisition module; wherein the data acquisition module is used to obtain ion data and concentration data of several air information types of the ion flow field area at different monitoring moments, the diffusion sensitivity coefficient acquisition module realizes the steps of the remote acquisition method based on the monitoring data of the free ion generator according to any one of claims 1-6 by calling a computer program to obtain a diffusion sensitivity coefficient, and the remote intelligent acquisition module adjusts a remote uploading frequency state of the concentration data of the air information types according to the diffusion sensitivity coefficient and uploads the concentration data to a cloud processing center for processing, thereby realizing the remote intelligent acquisition based on the monitoring data of the free ion generator.

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